Safe Sex Vs. No Sex - 1504 Words

Safe Sex vs. No Sex. What’s Realistic? A Case for Comprehensive Sex Education Teenagers have been having sex since the beginning of time. Instead of telling them â€Å"just don’t† shouldn’t we educate our teens so that they can be safe? The problem with traditional Abstince- Only-Until-Marriage is that instead of educating they use fear tactics to unsuccessfully keep children ignorant. This causes children to turn to other outlets like porn and other children their age, it’s like a bad game of telephone where people end up getting pregnant or catching STI’s. The other problem with Abstince-Only education is that it does not cover all of the subjects of sexuality, there are the obvious things such as STI’s and pregnancy prevention but there are†¦show more content†¦It is of extreme importance for the children of today to be educated well regarding human sexual health, so America can have a future generation with less STIs and less teen pregnancies. The groups and people who oppose sexual education, what compreh ensive sexual education is, and comprehensive sexual education vs. Abstinence only until marriage are why America need sexual education. Groups and people who still oppose comprehensive sex education have their own reasons for opposing what they do. A major group that still opposes comprehensive sex education are groups with religious affiliation. Not every church opposes comprehensive sex education, in fact one church the First United Methodist Church of Madison, Wisconsin will be implicating a comprehensive sex education curriculum (thinkprosses.org). Many churches still oppose comprehensive sex education though purely because it goes against the â€Å"morals† of the church, sex before marriage is a huge no-no in the religious world, many churches believe that by educating about sex it will advocate the pupils to have sex before marriage. This is not true, in fact even in religious settings pregnancy and STI rates have gone down, comprehensive sex education has even been sh own to reduce the age of first sex according to Advocatesforyouth.com a

Split Brain Research Essay Research Paper Chad free essay sample

Split Brain Research Essay, Research Paper Chad Stein PS 101 Dr. Rom 1. Gazzaniga, M.S. # 8220 ; One Brain or Two? # 8221 ; Scientific American. 1967. Rpt. In Forty Studies That Changed Psychology. Ed. Roger R. Hock. Engewood Cliffs: Prentice Hall, 1995. 2-11. 2. This article dealt with experiments that showed the different maps of the right and left hemisphere of the encephalon. It besides described the maps of the left and right hemisphere. # 8220 ; Your left encephalon is better at speech production, composing, mathematical computations, and reading, and is the primary centre for linguistic communication. Your right hemisphere, posses superior capablenesss for acknowledging faces, work outing jobs affecting spacial relationships, symbolic logical thinking, and artistic activities # 8221 ; ( 9 ) . The experiments were done to happen how each hemisphere of the encephalon procedure information. To make this the principal callosum was severed. This made it impossible for the two hemispheres of the encephalon to pass on with each other. When the principal callosum is severed it is referred to as the split encephalon consequence. We will write a custom essay sample on Split Brain Research Essay Research Paper Chad or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page The trials that were performed on the persons fell into three Fieldss: they were sight, hearing, and touch. All the trials sho wed that the above is true refering the maps of each hemisphere of the encephalon. The job that the writer was turn toing was the fact that the two hemispheres of the encephalon communicate with each other, and if the communicating is destroyed so the maps could be handled by one hemisphere better so the two combined. These experiments proved that there was a laterality in each hemisphere of the encephalon to execute certain undertakings. Even though a affiliated encephalon can execute the undertakings of both sides. 3. I think that the experiment showed precisely what it set out to make. Show that the separate hemispheres of the encephalon execute different undertakings. It besides showed that the encephalon is capable of executing these undertakings even when the principal callosum has been severed. Although some undertakings are performed better when the encephalon is able to pass on between the hemispheres. 4. The effects on psychological science are many. For case this research helps people understand the different parts of the encephalon, and how they work. Besides when hurts occur to the encephalon psychologist can find what the possible effects of the individual will be. Finally, psychologist will hold a better apprehension of how the human encephalon plants.

Twenty One Pilots by Twenty One Pilots free essay sample

Twenty One Pilots debut self-titled album, released in 2009, is an introspective whirlwind of poetic lyrics and interestingly dissonant accompaniment that culminates into a completely unique, diverse work.Although all of the songs on the album deal with themes of depression, insecurity, and other related themes, each song packages Twenty One Pilots message in a different way; some are fast and some slow, while others feature different accompanying instruments. The songs take structures that are singular not only to each other but to music of all genres and artists, and they include ingenious lyrics that shape the bands core message into deliciously difficult metaphors and symbols. Although the album deals with topics that have been seen before, especially in the alternative-punk genre in which Twenty One Pilots popularity is growing, it does so in a way that is makes the ages-old complaints seem brand new, as songwriter and lead singer Tyler Joseph uses his own personal experiences t o create a raw, true experience for the listener. We will write a custom essay sample on Twenty One Pilots by Twenty One Pilots or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page In terms of lyrical and musical content, the music on Twenty One Pilots is fresh and original in a daring and slightly frightening way that nevertheless captivates the listener.

The major themes in Our Day Out Essays

The major themes in Our Day Out Essays The major themes in Our Day Out Essay The major themes in Our Day Out Essay Essay Topic: Literature Our Day Out is set in inner city Liverpool in the mid 1970s. The fictional school is located in a neighbour with a high crime rate, drug use, prostitution, high unemployment, poverty and domestic abuse. The kids in the schools remedial class are all portrayed as economically unstable, poor and deprived children with different stories which reinforce the key ideas of the play. In 1981 there was rioting in Toxteth, a deprived district of Liverpool of which high unemployment rates were blamed for. The setting of the play acts as a prelude to the riots. Willy Russell adds elements of subtle humour which make the play funny and sad at the same time Early on in the play we are introduced to Carol Chandler who is evidently one of the poorest children in the class when she is revealed to be wearing a school uniform which doubles as a street outfit and a Sunday best, eating half a sandwich and clutching a carrier bag Here Russell is introducing us to one of the focal characters with a description which suggests that Carols family cant afford to buy here a school bag, have to share food and cant afford many clothes. This portrayal of Carol is important as it shows the signs of social deprivation and establishes her economic status. She describes Conwy as somewhere far away, I forget (in response to Les the lollipop mans question as to where the trips destination is). This also tells us she hasnt been far from home before since if shed been to Conwy before shed would know it is only about an hour away from Liverpool. We feel a little sorry for Carol and feel guilty about her having to live like that while our homes are often full of materialistic goods and appliances that we take for granted. When up on the cliff she refuses to return to the trip and wants to stay in Wales. We immediately get a sense of her naivety though it is a quality rather than a weakness and she clearly doesnt mean any harm at all. Carol has another quality of being appreciative of lifes simple things i. e. nature,since she cant posses materialistic products for a small price, this is a quality we all hope to have . She reveals to Mrs Kay-whom she looks to for mentoring, friendship and sometimes motherly love, which she cant get at home that she wants to live in one of them nice places with trees and that and underneath Mrs Kays encouraging facade, she knows Carol is stuck in the poverty cycle and wants to get out. Them nice places also shows Carols lack of education. This is tragic but Carols implicit disorganisation and forgetfulness accounts for the plays humour-I find this technique a very clever and powerful way of entertaining the audience and conveying the reality of these deprived childrens lifestyles. The play also focuses on Andrews, one of Carols peers in the progress class who has a similar lifestyle. Firstly, we learn that he smokes. He tells Reilly (an older ex-progress class student) to Gis a ciggy, in return for opening the window. Willy Russell continues with the theme of social deprivation by exposing Andrews mother as a prostitute when Digga refers to all them fellas she picks up This also tells us that Andrews mother not only has to practise promiscuity but has to risk her life on the streets of inner city Liverpool and we start to worry about Andrews mother putting her life at risk which makes us feel sorry. The aforementioned quote also shows bad education and not being able to speak properly. When Briggs tells Andrews off for smoking, he is told that Andrews mother doesnt take any notice but me dad, he belts me Ironically, Andrews is beaten because he wont give his father a cigarette. Andrews has a nasty life and we take pity and feel empathic but the aforementioned irony accounts for the plays humour, as does Briggs lack of understanding when he assumes Andrews father returns home because he is in the forces, when really, all he wants is the money. This portrayal of Andrews establishes the area of where the school is situated a deprived area and includes individual cases of families struggling to survive. Russell also uses Reilly, an ex-progress class student as a mouthpiece for his ideas and to convey aspects of social deprivation. We learn that Reillys dad works at the docks and hates it. Unrealistically, Briggs tells Reilly to tell his father to stop and take a look around. He may see things different then, an unrealistic expectation. Here we get a view of the docks through two different perspectives. To Briggs, an affluent middle class teacher the docks are historic and something to be proud of. To Reilly and his dad however, they are their means of employment which they have spent their lives trying to get away from. We also learn that Reilly has been motherless for ten years when Digga tells Briggs he cant swear on his mum sir shes been dead for ten years Reilly is in love with Susan, a young teacher in her early twenties who wouldnt go out with Reilly in a million years. Whether Reilly is being serious isnt revealed, but it is possible that Reilly is fishing for Susans money, in other words a goldigger. This possibility shows that Reilly doesnt have enough money of his own, so Russell is making us aware of social deprivation, a theme of the play. Reilly clearly has no respect for people above him in the school hierarchy, which shows lack of education, one of the plays theme. Later on in the play Susan turns the tables on Reilly and makes sudden advances and Russell manipulates the audience into believing she is being serious. She later tells him to stick to his own league and so forms a bond with 15-year-old Linda Croxley, a far more appropriate girlfriend for Reilly. We feel sorry for Reilly but being a motherless child has toughened him up and taught him to survive but his crush on Susan has a lot of comic element so contributes to the plays humour. Mr Briggs and Mrs Kay are focused upon heavily during the play who both have very different views on which teaching method is better for the remedial class. In a conversation with Colin, a young, less experienced teacher Briggs says well you have to risk being disliked if youre to do any good for these children and refers to Mrs Kays teaching method as woolly-headed liberalism. From this we can easily draw a conclusion that Mr Briggs doesnt think about the consequences of ruthlessness and all he is concerned about is positive results. Briggs obviously thinks because the kids missed out on a lot of education earlier in life they need some sort of intensive education technique if things are to be put right. He clearly sees respect from the kids as a nice extra when really it is essential. Also, when Mrs Kay changes the itinerary of the trip and takes the class to the zoo, Mr Briggs response tells us he doesnt want to deviate from the schedule and keep the kids bored, something in Briggs opinion would do the kids a bit of good. Colin later calls Mr Briggs a burke, and from this we can deduce that Mr Briggs is no more popular with his colleagues as he is with the students. On the other hand, Mrs Kay has a far more relaxed approach to educating the kids and a motherly, matriarchal attitude to the children-the two qualities awarding her street-cred with the kids. Mr Briggs sees this as a bad thing (he also thinks she has a motherly air) and he thinks if the antics in her department are anything to go by she always reminded me of a mother hen rather than a teacher. From this quote we can conclude that Mrs Kay is less popular with her colleagues, though the opinions of the other staff on her philosophical view of education are undisclosed. During a chat with Carol on the coach, Mrs Kay puts her arm around Carol and the stage directions at this point describe this as looking like a mother and daughter. Later on in the play she reveals explicitly that she is on the childrens side though this doesnt come as a surprise to the audience. This relationship is important since Carol has to look to her teacher for mentoring and motherly love which she cant get at home. This creates a possibility that Carols family maybe bad parents, socially deprived and not respectable. This reinforces the theme of social deprivation. All of these points establish Mr Briggs and Mrs Kays personalities and define the opposing forces. Willy Russell uses this opposition to manipulate the audience into wondering what will happen to the children with two completely different teachers taking control of them for the day, providing some of the plays humour. It also reveals societys opposing viewpoints about education. Throughout the play, stage directions are used to conjure up more explicit visions of what is going on. They are very important because if you are reading the play the more visual humour is hard to convey with words alone. At the start of the play, in the morning the kids are streaming in one direction. They [the kids] are shoving, rushing, ambling, leering and jeering. Here the strong use of verbs adds to the clarity of the description and leering and jeering suggests that there could be bullying going on. This shows lack of education- the kids obviously dont know what is right and wrong and have a poor sense of ethics (also evident in stealing the animals and taking them on to the coach). Stage directions can also express the humour that cant be conveyed with words alone. Just before leaving the zoo the animal keeper runs up to the coach polo-necked and wellied. Russell could have easily omitted that description but running in wellies is almost impossible. The image of someone running in something hard to run in e. g. stilettos, wellies, ski boots etc. is often used as a scene of slapstick humour- as opposed to the more dry, subtle humour used earlier on in the play. Also, animals appear from every conceivable hiding place and the coach is a menagerie. This stage direction pictures animals swarming around a small area(the coach), scuttling from side to side, jumping out of luggage lockers and generally causing chaos. This scene accounts for the plays humour but in my opinion, it is hard to fully appreciate without a graphical representation. The visual medium of TV allows Willy Russell to use the device of visual metaphor. While the class are in the zoo they are compared to a captive bear in an implicit way and are trapped in different ways. The bear is literally trapped in the pit for visitors and tourists to see and it cant do much, if anything to get out. The children, on the other hand are trapped in a more metaphorical way and stuck at their level in the social hierarchy and in the poverty cycle. From Briggs line dont forget it was born in captivity so it wont know any other life, we get the impression Briggs feels the children should stay working class rather than climb the pecking order to a middle or upper class rank and mixing with the more affluent Liverpudlians, while keeping their coarse and vulgar demeanours. From this we can tell Briggs is politically right-wing and if real, would have been one of the many Britons who decided it was time for James Callaghan to call it a day it 1979. Mrs Kay thinks the children deserve better but is unsure as to which route in life is best for the kids. Russell uses this technique again at the castle when comparing Mr Briggs old-fashioned teaching methods to the archaic, medieval castle-despite the fact he is younger than Mrs Kay, though you wouldnt think so. At the castle there is also a showdown between Mrs Kay and Mr Briggs, and the castle provides the perfect backdrop for it. Mrs Kays idea of visiting the modern zoo goes well with her modern philosophical view of education. However, Mrs Kays expectations of the kids are a bit too low and arent likely to bring out the full potentials of the kids. Conversely, Briggs are too high and are less realistic than Mrs Kays. Although neither teachers expectations are perfect (expectations of a teacher who gives the kids push and support simultaneously would be), Mrs Kays are more appropriate on the whole, and despite being too soft on the kids, Mrs Kay seems to know it is better to under-expect rather than to over-expect like Mr Briggs does because his views of education are far from appropriate and are no better for the kids than Mrs Kays. Willy Russell moves the play to a more dramatic climax which creates suspense-a literary device previously unused in the play. Carols naivety is reinforced by her explicit desire to remain in Wales. At this point Briggs doesnt change in personality but is now powerless and has no way of controlling Carols erratic and suicidal behaviour though knowing Briggs he is probably more concerned about being struck off and a legal inquiry than Carols state of mind and only told her she had hope to avoid the two aforementioned crises. When Briggs changes his ways Russell manipulates the audience into thinking Briggs is changing permanently and he will help the kids catch up and excel in life. However our expectations are dashed when the class returns to the city. When Reilly describes the city as horrible when you come back to it, Russell tells the audience that the children must be used to their neighbourhood after 13-15 years of entrapment in the inner city. Linda is unsure what Reilly is talking about which shows that she was taken in by the trip. Russells message to the audience is that living in the inner city of Liverpool can toughen one up and as a result, enables the kids to survive almost anything. The ending is disappointing yet realistic and Briggs making a fool of himself through singing a ridiculous song in a cowboy hat adds a humorous side to this sombre scene.

Provide an Ethical Argument against the Use of Dolphins in the US Navy Essay

Provide an Ethical Argument against the Use of Dolphins in the US Navy for Military Purposes - Essay Example Our villages are the most avidly practicing customary and traditional Subsistence users in the United States. The socioeconomic characterization of our region is similar to a Lesser Developed Country (LDC). Before the Magnuson Act, our people stood on the shores of their seasonal food camps and watched international fleets fish off our coasts, destroying species and stocks in their wake and affecting our Subsistence needs. Most prevalent were the Japanese; whose economists dubbed our region, "The Fourth World," to describe the phenomena of third world standard of living conditions within a first world country. Since the early 1970's, and prior to the MIAPA, AVCP subcontracted marine mammal studies and traditional knowledge reports through scientific and technical staff of Nunam. Kitlutsisti (Stewards of the Land). We joined in lobbying the UN and the U.S. and Russian governments to ban high seas driftnet fishing and succeeded. Nunam Kitlutsisti was eventually absorbed into the Department of Natural Resources (DNR) of AVCP. Since then, AVCP has been an actively participating in meetings with the Indigenous Peoples Council on Marine Mammals (IPCoMM), the Eskimo Walrus Commission (EWC), and the Alaska Beluga Whale Committee (ABWC). For decades, AVCP has tried to improve the growing-pains of the assimilation process for Yup'ik Cup'ik immersion into Western economy standards, while maintaining an enduring Native culture. AVCP coordinates regional, social, educational, economic and land / resource management programs. The DNR is extensively involved in programs with the Yukon Delta National Wildlife Refuge (YDNWR), and the Alaska Department of Fish and Game. On shared resource issues we work extensively with other Native regional groups along with the Washington Department of Fish and Wildlife, Oregon Department of Fish and Wildlife, and the California Department of Fish and Game. AVCP has been co-managing programs with YDNWR and the Togiak National Wildlife Refuge in the following programs: Western Alaska Brown Bear Management Area Agreement, Qauilnguut (Kilbuck) Caribou Herd Management Plan, Lower Yukon Moose Management Plan, Yukon-Kuskokwim Delta Goose Management Plan (Waterfowl Conservation Committee), Imarpigmiut Ungungsiit Murilkestfit (IUM) (Watchers of the Sea Mammals), Lower Kuskokwim Moose Management Plan, Kuskokwim River Drainage Fisheries Association, Kwethluk Counting Tower (Salmon spawning monitoring), Lower Kuskokwim Moose Management Area. Imarpigmiut Ungungsht Murilkestiit (IUM) (Watchers of the Sea Mammals) AVCP / IUM currently represents 26 coastal villages and voices concerns regarding marine mammal Subsistence and the health and viability of the Bering Sea. With the development of an Iced Seals Commission under our marine mammal program, we are fully prepared to involve all Iced Seals Subsistence user groups in the State of Alaska. AVCP / IUM intends to develop the scientific, traditional and technical expertise we need to become full partners in cooperative management to the benefit of federal partners and for the conservation and Subsistence use

Analyze the impact of technology on a field of study of your choice Essay

Analyze the impact of technology on a field of study of your choice - Essay Example It is observed that today mobile devices proliferate in corporate environments as these devices can be easily connected to company networks. Although these devices have become an integral part of the modern life, they raise certain potential challenges to the cyber security. According to experts, it is relatively easy to hack mobile devices as compared to other computer devices. Hence, they are highly prone to data theft. In addition, today mobile devices are widely used for cyber crimes because this practice reduces the chances of being caught. Despite numerous advantages including high productivity and greater convenience, mobile devices raise severe threats to cyber security. According to a study conducted among IT professionals (as cited in Dimensional research, 2012), nearly 89% of the organizations connect mobile devices to their corporate networks. Roughly 65% participants responded that their employees used private mobile devices to access the corporate networks. The particip ants also indicated the major mobile platforms used to access corporate networks were Apple iOS (30%), BlackBerry (29%), and Android (21%). Majority of the participants (64%) argued that there has been an increase in mobile devices-related security risks to their organizations over the past two years (Dimensional research, 2012). ... For instance, it is often observed that employees connect their personal mobile devices to unprotected company networks like Wi-Fi in order to access internet. This practice increases the chances of malware attacks on the company networks and subsequently the malware-affected network may cause to lose the valuable business information stored in the company’s mobile devices. In addition, fraud employees can easily copy sensitive company information to their personal mobile devices within seconds and such issues can challenge the firm’s cyber security. Similarly, mobile devices are extremely prone to theft and loss due to their small size and high portability. If mobile devices reach the hands of third parties, they can access the data stored in it using highly advanced applications even though those devices are password-protected. Due to their small size, it is easy for external people to steal mobile devices. Evidently, data theft by individuals would more harmfully aff ect an organization than data loss due to malware attacks. In case of data theft, there might be a possibility of leaking the sensitive data to business competitors. This type of data loss or theft from mobile devices would extremely impact individuals too. Probably, people will store their family-related images and videos and other personal documents on their mobile devices. If such data are accessed by unauthorized people, this would cause great troubles to the users. Kuspriyanto and Noor (2012) point that the use of NFC (near field communication) in mobile payments systems make mobile platforms vulnerable to financially motivated cybercrimes. Evidently, such issues often cause users to suffer huge financial losses. Today,

Symbolic Learning Methods Essay Example for Free

Symbolic Learning Methods Essay Abstract In this paper, performance of symbolic learning algorithms and neural learning algorithms on different kinds of datasets has been evaluated. Experimental results on the datasets indicate that in the absence of noise, the performances of symbolic and neural learning methods were comparable in most of the cases. For datasets containing only symbolic attributes, in the presence of noise, the performance of neural learning methods was superior to symbolic learning methods. But for datasets containing mixed attributes (few numeric and few nominal), the recent versions of the symbolic learning algorithms performed better when noise was introduced into the datasets. 1. Introduction The problem most often addressed by both neural network and symbolic learning systems is the inductive acquisition of concepts from examples [1]. This problem can be briefly defined as follows: given descriptions of a set of examples each labeled as belonging to a particular class, determine a procedure for correctly assigning new examples to these classes. In the neural network literature, this problem is frequently referred to as supervised or associative learning. For supervised learning, both the symbolic and neural learning methods require the same input data, which is a set of classified examples represented as feature vectors. The performance of both types of learning systems is evaluated by testing how well these systems can accurately classify new examples. Symbolic learning algorithms have been tested on problems ranging from soybean disease diagnosis [2] to classifying chess end games [3]. Neural learning algorithms have been tested on problems ranging from converting text to speech [4] to evaluating moves in backgammon [5]. In this paper, the current problem is to do a comparative evaluation of the performances of the symbolic learning methods which use decision trees such as ID3 [6] and its revised versions like C4.5 [7] against neural learning methods like Multilayer perceptrons [8] which implements a feed-forward neural network with error back propagation. Since the late 1980s, several studies have been done that compared the performance of symbolic learning approaches to the neural network techniques. Fisher and McKusick [9] compared ID3 and Backpropagation on the basis of both prediction accuracy and the length of training. According to their conclusions, Backpropagation attained a slightly higher accuracy. Mooney et al., [10] found that ID3 was faster than a Backpropagation network, but the Backpropagation network was more adaptive to noisy data sets. Shavlik et al., [1] compared ID3 algorithm with perceptron and backpropagation neural learning algorithms. They found that in all cases, backpropagation took much longer to train but the accuracies varied slightly depending on the type of dataset. Besides accuracy and learning time, this paper investigated three additional aspects of empirical learning, namely, the dependence on the amount of training data, the ability to handle imperfect data of various types and the ability to utilize distributed output encodings. Depending upon the type of datasets they worked on, some authors claimed that symbolic learning methods were quite superior to neural nets while some others claimed that accuracies predicted by neural nets were far better than symbolic learning methods. The hypothesis being made is that in case of noise free data, ID3 gives faster results whose accuracy will be comparable to that of back propagation techniques. But in case of noisy data, neural networks will perform better than ID3 though the time taken will be more in case of neural networks. Also, in the case of noisy data, performance of C4.5 and neural nets will be comparable since C4.5 too is resistant to noise to an extent due to pruning. 2. Symbolic Learning Methods In ID3, the system constructs a decision tree from a set of training objects. At each node of the tree the training objects are partitioned by their value along a single attribute. An information theoretic measure is used to select the attribute whose values improve prediction of class membership above the accuracy expected from a random guess. The training set is recursively decomposed in this manner until no remaining attribute improves prediction in a statistically significant manner when the confidence factor is supplied by the user. So, ID3 method uses Information Gain heuristic which is based on Shannon’s entropy to build efficient decision trees. But one dis advantage with ID3 is that it overfits the training data. So, it gives rise to decision trees which are too specific and hence this approach is not noise resistant when tested on novel examples. Another disadvantage is that it cannot deal with missing attributes and requires all attributes to have nominal values. C4.5 is an improved version of ID3 which prevents over-fitting of training data by pruning the decision tree when required, thus making it more noise resistant. 3. Neural Network Learning Methods Multilayer perceptron is a layered network comprising of input nodes, hidden nodes and output nodes [11]. The error values are back propagated from the output nodes to the input nodes via the hidden nodes. Considerable time is required to build a neural network but once it is done, classification is quite fast. Neural networks are robust to noisy data as long as too many epochs are not considered since they do not overfit the training data. 4. Evaluation Design For the evaluation purposes, a free and popular software tool called Weka (Waikato Environment for Knowledge Acquisition) is used. This software has the implementations of several machine learning algorithms made easily accessible to the user with the help of graphical user interfaces. The training and the test datasets have been taken from the UCI machine learning repository. Two different types of datasets will be used for the evaluation purposes. One type of datasets contain only symbolic attributes (Symbolic Datasets) and the other type contain mixed attributes (Numeric Datasets). Performance of the different learning methods will be evaluated using the original datasets which do not contain any noise and after introducing noise into them. Noise is introduced in the class attributes of the datasets by using the ‘AddNoise’ filter option in Weka which adds the specified percentage of noise randomly into the datasets. Symbolic Datasets are those which contain only symbolic attributes. Symbolic learning methods like ID3 and its recent developments can be run only on datasets where all the attributes are nominal. In Weka, these nominal attributes are automatically converted to numeric ones for neural network learning methods. So, preprocessing is not required in this type of datasets. Numeric Datasets are those which contain few nominal and few numeric attributes. Since symbolic learning methods like ID3 and its recent developments can be run only on datasets where all the attributes are nominal, these datasets first need to be preprocessed. A ‘Discretize’ filter option available in Weka is used to discretize all the non-symbolic attribute values into individual intervals so that each attribute can now be treated as a symbolic one. Initially, the entire data being considered is randomized. Two types of evaluation techniques are being used to analyze the data. (a) Percentage Split: In general, the data will be split up randomly into training data and test data. In the experiments conducted, the data will be split such that training data comprises 66% of the entire data and the rest is used for testing. (b) K-fold Cross-validation: In general, the data is split into k disjoint subsets and one of it is used as testing data and the rest of them are used as training data. This is continued till every subset has been used once as a testing dataset. In the experiments conducted, 5-fold cross validation was done. 5. Experimental Results Experiments were conducted on two symbolic datasets and two numeric datasets. The two symbolic datasets are tic-tac-toe and chess. The two numeric datasets are segment and teacher’s assistant evaluation (tae). DataSet 1 : TIC-TAC-TOE (a) 5-fold cross validation (i)Without any noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.03 6.16 0.02 0.06 0.01 % correct 67.4322 81.8372 75.8873 73.5908 71.2944 % incorrect 28.0793 18.1628 24.1127 26.4092 28.7056 % not classified 4.4885 0 0 0 0 Time to build 0.06 6.35 0.06 0.01 0.02 % correct 86.1169 97.4948 85.8038 87.5783 83.1942 % incorrect 11.691 2.5052 14.1962 12.4217 16.8058 % not classified 2.1921 0 0 0 0 (b) Percentage split with training data being 66% and the rest is testing data (i)Without Noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii)Percentage of Noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.05 6.5 0.01 0.01 0.02 % correct 85.5828 97.546 83.1288 88.0368 82.2086 % incorrect 11.0429 2.454 16.8712 11.9632 17.7914 % not classified 3.3742 0 0 0 0 Time to build 0.04 6.15 0.02 0.02 0.01 % correct 68.4049 80.6748 73.9264 72.3926 71.4724 % incorrect 28.2209 19.3252 26.0736 27.6074 28.5276 % not classified 3.3742 0 0 0 0 For the tic-tac-toe dataset, in the presence of noise, neural nets had better prediction accuracies than all the other algorithms as expected. Though C4.5 gives better accuracy than ID3, its accuracy is still lower in comparison to Neural Nets. If the pruning factor (confidence factor was lowered) was increased, the prediction accuracies of C4.5 dropped a little. But in the absence of noise, the performances of ID3 and Multilayer Perceptron should have been comparable. But the performance of Multilayer Perceptron is quite superior to ID3. DataSet 2 : CHESS (a) 5-fold cross validation (i)Without any noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.36 47.75 0.21 0.18 0.19 % correct 81.1952 86.796 89.0488 84.6683 88.4856 % incorrect 18.8048 13.204 10.9512 15.3317 11.5144 % not classified 0 0 0 0 0 Time to build 0.21 47.67 0.15 0.05 0.1 % correct 99.562 97.4656 99.3742 99.3116 99.2178 % incorrect 0.438 2.5344 0.6258 0.6884 0.7822 % not classified 0 0 0 0 0 (b) Percentage split with training data being 66% and the rest is testing data (i)Without Noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii)Percentage of Noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.33 41.73 0.24 0.19 0.19 % correct 80.1288 85.7406 87.5805 82.6127 87.6725 % incorrect 19.8712 14.2594 12.4195 17.3873 12.3275 % not classified 0 0 0 0 0 Time to build 0.13 43.55 0.06 0.06 0.08 % correct 99.448 97.1481 99.08 98.988 99.08 % incorrect 0.552 2.8519 0.92 1.012 0.92 % not classified 0 0 0 0 0 For the chess dataset, in the absence of noise, the performance of ID3 is better than that of Multilayer perceptron and takes lesser time. For the noisy data, back propagation predicts better accuracies than that of ID3 as expected, but the performance of C4.5 is slightly higher than back propagation. The reason for this could be that the feature space in this dataset is more relevant. So, C4.5 builds a tree and prunes it to get a more efficient tree. DataSet 3 : SEGMENT (a) 5-fold cross validation (i) Without any noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.07 9.64 0.04 0.04 0.03 % correct 68.9333 80.8667 81.2667 79.6 80.5333 % incorrect 21.3333 19.1333 18.7333 20.4 19.4667 % not classified 9.7333 0 0 0 0 Time to build 0.05 10.3 0.02 0.23 0.12 % correct 88.0667 90.6 91.6 94 94.3333 % incorrect 5.2 9.4 8.4 6 5.6667 % not classified 6.7333 0 0 0 0 (b) Percentage split with training data being 66% and the rest is testing data (i) Without Noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of Noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.07 11.73 0.03 0.04 0.03 % correct 72.9412 82.549 82.1569 82.549 81.3725 % incorrect 19.6078 17.451 17.8431 17.451 18.6275 % not classified 7.451 0 0 0 0 Time to build 0.06 9.87 0.03 0.02 0.03 % correct 89.8039 87.6471 92.1569 93.7255 90.1961 % incorrect 4.1176 12.3529 7.8431 6.2745 9.8039 % not classified 6.0784 0 0 0 0 Segment, being a numeric dataset, all the attribute values had to be discretized before running the algorithms. In the absence of noise, ID3 performs slightly better than back propagation and the performance of J48 (implementation of C4.5 in Weka) is much better than ID3 and backpropagation. But a very interesting observation was found. In the absence of noise, the performance of an unpruned tree generated by C4.5 was quite superior to the rest. In the presence of noise, the performances of back propagation and C4.5 were comparable. DataSet 4 : TAE (a) 5-fold cross validation (i) Without any noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of noisy data = 10% Time to % % build correct incorrect ID3 0.02 53.6424 37.0861 Multilayer Perceptron 0.16 38.4106 61.5894 J48 0.02 52.9801 47.0199 C4.5 unpruned 0.01 56.2914 43.7086 C4.5 confidence factor = 0.1 0.01 54.3046 45.6954 (b) Percentage split with training data being 66% and the rest is testing data (i) Without Noise: Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 (ii) Percentage of Noisy data = 10% Classifiers ID3 Multilayer Perceptron J48 C4.5 unpruned C4.5 confidence factor = 0.1 Time to build 0.01 0.17 0.01 0.01 0.01 % correct 38.4615 44.2308 44.2308 50 44.2308 % incorrect 40.3846 55.7692 55.7692 50 55.7692 % not classified 21.1538 0 0 0 0 Time to build 0.02 2.23 0.03 0.02 0.01 % correct 44.2308 57.6923 51.9231 55.7692 42.3077 % incorrect 34.6154 42.3077 48.0769 44.2308 57.6923 % not classified 21.1538 0 0 0 0 Classifiers % not classified 0 0 0 0 0 Time to build 0.02 0.18 0.02 0.01 0.01 % correct 54.3046 54.9669 48.3444 50.9934 47.0199 % incorrect 35.0993 45.0331 51.6556 49.0066 52.9801 % not classified 10.596 0 0 0 0 TAE, being a numeric dataset, its attribute values had to be discretized too before running the algorithms. But after observing the results, it is very clear that the random discretization provided by Weka did not generate good intervals due to which the overall accuracy predicted by all the methods is quite poor. Again, interestingly an unpruned tree built by C4.5 seems to give high prediction accuracies relative to the rest in most of the cases. In this case, for cross-validation approach and noisy data, surprisingly the performance of back-propagation was very poor. One reason for this could be that only few epochs of the training data were run to build the neural network. In the absence of noise, accuracy prediction of Multilayer perceptron was either comparable or greater than that of ID3. 6. Conclusion No single machine learning algorithm can be considered superior to the rest. The performance of each algorithm depends on what type of dataset is being considered, whether the f eature space is relevant and whether the data contains noise. In the absence of noise, in some cases, the performance of ID3 was comparable or sometimes better than back-propagation and was faster but in some cases Multilayer perceptron performed better. When noisy datasets were considered, back propagation definitely did better than ID3 though it took more time to build the neural network. But in the presence of noise, in some cases, C4.5 gave faster and better results when the attributes being considered were relevant. But some surprising observations were made when the attribute values of the numeric datasets were discretized, the prediction accuracy of an unpruned tree generated by C4.5 algorithm was much higher than the rest. This shows that the unpruned tree generated by C4.5 is not the same as that generated by ID3. References: 1.Mooney, R., Shalvik, J., and Towell, G. (1991): Symbolic and Neural Learning Algorithms An experimental comparison, in Machine Learning 6, pp. 111-143. 2. Michalski, R.S., Chilausky, R.L. 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