Assessment analysis Essay Example | Topics and Well Written Essays - 500 words

Assessment analysis - Essay Example In the new expectations though, the items titled "Background" and "Statement of the problem" become one content item and the items titled "Purpose" and "Research questions" also become one content item. Also, the current first chapter has an additional content item titled "School Fighting using Deadly Weapons", whose content should exist as part of the first chapter's introduction described in the new expectations, and another item titled "Significance of the study", whose content should exist as part of the content item "Purpose and research questions" (2007, p. 3, 6). At the same time, the current first chapter lacks the item titled "Content of the action research study/project" which should come between the items "Purpose and research questions" and "Definitions of terms" (2007, p. 7-8). Regarding the order of content items in the first chapter, a difference can be seen. In the currently written chapter, the order is as follows: Assumptions, Delimitation, Definitions (of terms), and Summary, while in the new expectations, the order is as follows: Content of the action research study/project, Definitions of terms, and Summary.

Gun Control or any other interesting philosophical topic., i don't Essay

Gun Control or any other interesting philosophical topic., i don't mind - Essay Example Such essential rights allow the citizen of United States as the independent citizen without any control. The expression â€Å"gun control† has different meanings for different citizens and there are some counter laws have opposed the condition for the last many years that gives protection to firearms. Under the gun control, it involves the rules and regulations developed by the government that bounds the right of the a gun users in order to buy, carry or operate the firearm in order to eradicate the negativities of the gun owning in the form of robbery, theft, abduction, murder and other criminal activities. This right limitation matches the Kant’s model that explains that the morality of the action depended upon the intention of the individual and not on the consequence of that act (Tampio 68). The issue under question is the limiting of the citizen’s right to carry the arms will not match the interest of everyone. For the gun control matter, there are two major groups that have opposite believes and includes individual rights and utilitarianism. Both the theories cannot exist at one time and it is completely against the utilitarianism to grant the full rights to the citizen to own and freely use the gun and ammunitions. By using this theory, the government derived the gun control rule that is in violation to the complete freedom and human rights of the citizen. However, from the constitution point of view, it is absolutely lawful to regulate firearms but on the ethical grounds, it is not right. The second amendment has the term â€Å"well regulated† that is subjected to many arguments. According to some people, the expression well regulated meant to be the controlling aspect or the ruling aspect from the government perspective. On the other hand, there are other meanings of the phrase which is not acceptable by many individuals. In other words, regulated can be considered as properly operating for the benefits of the country. It is no denial in the fact that reduction in the criminal activities considered as the better option by everyone. Gun lawyers are of the view that it is the possession of the gun that motivated the criminal to do the act and thus, gun has a vital role in the increment of the criminal activities. The said words are the main line for the anti-gun campaign. The debate that guns is used for conducting the crime and possession of guns are harmful based on the immediate function; therefore, it will be in the interest of the nation to outlaw the gun carrying and use. on the contrast, there are certain lawyers like Gary Kleck who is also the professor of criminology in Florida state university presented the statistics that citizens of U.S are protecting themselves 2.4 million times each year from the criminals by making use of their guns. The study was conducted in 1993 by the professor and more than 6000 families were involved in the survey study. The bureau of justice had the statics of 1.1 m illion criminal acts that were enforced by the use of gun in 1992, that revealed a relationship between the high use of gun power and the lowering of criminal activities. Under the light of legal gun control policy, practicing of filing of cases against the people became common, in them most of the cases were subjected to gun producers, who are not only producing but spreading the deadly weapon. While the lawsuit in between US and Emerson, a

Capstone Project -2 Essay Example | Topics and Well Written Essays - 1500 words

Capstone Project -2 - Essay Example The theory or concept of self-management of Type 1 Diabetes or Juvenile Onset Diabetes that are found in both children and adolescents states that process, activities and goals are its three essential attributes (Schilling et al., 2002). In another way, Hughes (2010) describes these attributes as knowledge/education, relationship/partnership, self-monitoring/self-care and one umbrella attribute, the action-directed skills. This equates to an active and proactive process being conducted on a daily basis, on a lifelong duration and involvement of shifting and shared responsibility of diabetes care tasks, and decision-making between the child and parent (Schilling et al., 2002). This self-management theory incorporates all survival strategies for a patient with Type 1 Diabetes so that he is able to manage the disease and yet look forward to years of growth and productivity in his later life. This is exactly my proposed solution to the disease, recognizing its no-cure properties and its presence in the body system of the child until his entire lifetime. The theory will be incorporated in this Capstone project by forming a Type 1 Diabetes Clinic in which all information about the disease itself will be made available in the clinic and translated into a simple language that can be easily shared and taught by professional nurses to the patients, their families and relatives, and other concerned individuals. Self-management will be thoroughly covered in terms of all available media resources in the aim that training the â€Å"caregivers† of Type 1 diabetic patients will contribute significantly to the positive growth and progress of the patients and will be gladly anticipated by family members thereby reducing the incidence of any form of stress, burn-out and losing of hope when dealing with the disease. Thus the strategies of the Type 1 Diabetes Clinic project is directed towards, thorough education, personalized caring of patients, positive and proactive deliv ery of Diabetes-management methods and, consistent positive anticipation practices of the future for diabetic patients in order to promote mental wellness as well. This project is expected to support the implementation of a quality life for Type 1 Diabetes’ patients, family and relatives, and consequently resulting to a healthy metabolic control and, development of the patients (Faulkner and Chang, 2007). References: Hughes, L. (2010). Self-Management: an evolutionary concept analysis [Online]. University of Victoria, 72 pp. Available at: http://dspace.library.uvic.ca:8080/bitstream/handle/1828/4057/Hughes_Lori_MN_2010.pdf?sequence=1 [15 Jan 2013]. Faulkner, M.S. and Chang, L. (2007), Family Influence on Self-Care, Quality of Life, and Metabolic Control in School-Age Children and Adolescents with Type 1 Diabetes. Journal of Pediatric Nursing, 22(1):59-68. Schilling, L. S. , Grey, M. and Knafl, K. A. (2002), The concept of self-management of type 1 diabetes in children and ado lescents: an evolutionary concept analysis [Abstract]. Journal of Advanced Nursing, 37:  87–99. DOI:  10.1046/j.1365-2648.2002.02061.x Instructions: Assignment 2 Write a paper (1,500 words) in which you analyze and appraise each of the

Animal Equality Assignment Example | Topics and Well Written Essays - 250 words

Animal Equality - Assignment Example They try to do this by educating individuals about speciesism and veganism. They carry out activities which would help the general public to know the truth about what is happening in the society in regard to the animal rights. In simple words, Animal Equality urges people to stop using animals to fulfill their own needs and wants. Speciesism here refers to a form of discrimination which is used against species which are non-human. Animal Equality tries to urge people to stop exploiting animals by making them aware of the concepts of Speciesism. Similarly, Veganism is a concept of urging people to consume a diet which is more associated with plants and does not in any way exploit animals. Both these concepts together are the basis for this organization in providing the animals with their rights. Animal Equality has carried out several actions so as to decrease or eradicate animal exploitation. This involves activities such as raiding slaughterhouses and places where animals could be e xploited. By raiding and finding anything against animal rights the organization is making aware the general public about the injustice that is being laid upon the animals nowadays. Similarly, they also hold street protests to voice their concerns regarding animals if they feel any discrimination is being done against these species. Animal Equality is an organization which is doing its best to keep its purpose alive and is working towards the goal to provide the animals with the rights which they deserve.

Capstone Project -2 Essay Example | Topics and Well Written Essays - 1500 words

Capstone Project -2 - Essay Example The theory or concept of self-management of Type 1 Diabetes or Juvenile Onset Diabetes that are found in both children and adolescents states that process, activities and goals are its three essential attributes (Schilling et al., 2002). In another way, Hughes (2010) describes these attributes as knowledge/education, relationship/partnership, self-monitoring/self-care and one umbrella attribute, the action-directed skills. This equates to an active and proactive process being conducted on a daily basis, on a lifelong duration and involvement of shifting and shared responsibility of diabetes care tasks, and decision-making between the child and parent (Schilling et al., 2002). This self-management theory incorporates all survival strategies for a patient with Type 1 Diabetes so that he is able to manage the disease and yet look forward to years of growth and productivity in his later life. This is exactly my proposed solution to the disease, recognizing its no-cure properties and its presence in the body system of the child until his entire lifetime. The theory will be incorporated in this Capstone project by forming a Type 1 Diabetes Clinic in which all information about the disease itself will be made available in the clinic and translated into a simple language that can be easily shared and taught by professional nurses to the patients, their families and relatives, and other concerned individuals. Self-management will be thoroughly covered in terms of all available media resources in the aim that training the â€Å"caregivers† of Type 1 diabetic patients will contribute significantly to the positive growth and progress of the patients and will be gladly anticipated by family members thereby reducing the incidence of any form of stress, burn-out and losing of hope when dealing with the disease. Thus the strategies of the Type 1 Diabetes Clinic project is directed towards, thorough education, personalized caring of patients, positive and proactive deliv ery of Diabetes-management methods and, consistent positive anticipation practices of the future for diabetic patients in order to promote mental wellness as well. This project is expected to support the implementation of a quality life for Type 1 Diabetes’ patients, family and relatives, and consequently resulting to a healthy metabolic control and, development of the patients (Faulkner and Chang, 2007). References: Hughes, L. (2010). Self-Management: an evolutionary concept analysis [Online]. University of Victoria, 72 pp. Available at: http://dspace.library.uvic.ca:8080/bitstream/handle/1828/4057/Hughes_Lori_MN_2010.pdf?sequence=1 [15 Jan 2013]. Faulkner, M.S. and Chang, L. (2007), Family Influence on Self-Care, Quality of Life, and Metabolic Control in School-Age Children and Adolescents with Type 1 Diabetes. Journal of Pediatric Nursing, 22(1):59-68. Schilling, L. S. , Grey, M. and Knafl, K. A. (2002), The concept of self-management of type 1 diabetes in children and ado lescents: an evolutionary concept analysis [Abstract]. Journal of Advanced Nursing, 37:  87–99. DOI:  10.1046/j.1365-2648.2002.02061.x Instructions: Assignment 2 Write a paper (1,500 words) in which you analyze and appraise each of the

Sexual harassment in the workplace Research Paper

Sexual harassment in the workplace - Research Paper Example This is a crucial topic to discuss since women have made significant progress towards achieving respect and equality at the work place, but there are some challenges that face their efforts. Sexual Harassment in the Workplace Introduction For many years, women’s rights have been under threat, whether it is within the family set up or any other place outside the family set up. In many countries, the civil society organizations are in the forefront fighting for women equality in all spheres of life. However, equality have failed to prevail in various areas whereby women face lack protection from violence, political, economic, and personal security, and also lack of full access to sexual and reproductive health. It is worth mentioning that women have come out in large numbers to join the workforce around the world (Kaushik, 2003). Discussion The need to be financially independent is a significant contributing factor to these advancements in women life. The increased number of wom en in the workplace is marked with increased vulnerability of women to acts of sexual harassment. This form of women mistreatment is said to be the oldest and most widely spread form of women harassment. In addition, it affects lives of all women irrespective of their culture, age, religion, income, race or class. Experts point out that sexual harassment is a tool that men use to portray their dominance on women since they are considered to be the weaker gender. The most affected women in the society are those focused on fighting the patriarchal system (Shahira & Widad, 2009). Sexual harassment being about power puts women in an inferior position. There are women who respond to acts of sexual harassment in extremely strict manner, but the largest number of women continues to suffer in silence. Those who persevere with acts of sexual harassment do so due to fear of stigma, hostility, ridicule, and discrimination. At the work place, the management must ensure that women are protected from acts of sexual harassment as well as handling such cases in a free and fair manner when they arise. However, due regard is not paid to such cases, which aggravates the issue of sexual harassment in such organizations (Cobb-Clark, 2009). Over a long period of time, many countries have failed to recognize the issue of sexual harassment as a key violation of human rights. This has caused the lack of clear rules and methods to deal with cases of sexual harassment. However, countries such as India have made tremendous progress in combating offences on sexual harassment. In India, the Supreme Court recognizes acts of sexual harassment as unacceptable acts, which cannot be condoned at work places. The increased number of women at workplaces and the closeness between men and women calls for clear guidelines on how to deal sexual harassment in all countries (Shahira & Widad, 2009). Women are known to be excellent in whatever they do. Going by this fact, providing a safe work environment for women implies that their productivity at the workplace will be optimum. Therefore, any organization that is to excel in its operations should take advantage of its women work force. Study based evidence indicates that, in work places where women are in authority, there are less cases of sexual harassments towards women. This observation implies that main perpetrators of acts of sexual harassment are men in authority. This gives men in such positions a lot advantages since the affected women fear

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. (1980): Learning by being told and learning from examples An experimental comparison of two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis, in Policy Analysis and Information Systems, 4, pp. 125-160. 3. Quinlan, J.R. (1983): Learning efficient classification procedures and their application to chess end games in R.S. Michalski, J.G. Carbonell, T.M. Mitchell (Eds.), in Machine learning: An artificial intelligence approach (Vol. 1). Palo Alto, CA: Tioga. 4. Sejnowski, T.J., Rosenberg, C. (1987): Parallel networks that learn to pronounce English text, in Complex Systems, 1, pp. 145-168. 5. Tesauro, G., Sejnowski, T.J. (1989): A p arallel network that learns to play backgammon, in Artificial Intelligence, 39, pp. 357-390. 6. Quinlan, J.R. (1986): Induction on Decision Trees, in Machine Learning 1, 1 7. Quinlan, J.R. (1993): C4.5 – Programs for Machine Learning. San Mateo: Morgan Kaufmann. 8. Rumelhart, D., Hinton, G., Williams, J. (1986): Learning Internal Representations by Error Propagation, in Parallel Distributed Processing, Vol. 1 (D. Rumelhart k J. McClelland, eds.). MIT Press. 9. Fisher, D.H. and McKusick, K.B. (1989): An empirical comparison of ID3 and backpropagation, in Proc. of the Eleventh International Joint Conference on Artificia1 Intelligence (IJCAI-89), Detroit, MI, August 20-25, pp. 788-793. 10. Mooney, R., Shavlik, J., Towell, G., and Gove, A.(1989): An experimental comparison of symbolic and connectionist learning algorithms, in Proc. of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), Detroit, MI, August 20-25, pp. 775-780. 11. McClelland, J. k Rumelhart, D. (1988). Explorations in Parallel Distributed Processing, MIT Press, Cambridge, MA.