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Introduction to Classification: Part II

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- 1. Data Mining Classification: Alternative Techniques<br />
- 2. Rule-Based Classifier<br />Classify records by using a collection of “if…then…” rules<br />Rule: (Condition) y<br />where <br /> Condition is a conjunctions of attributes <br /> y is the class label<br />LHS: rule antecedent or condition<br />RHS: rule consequent<br />
- 3. Characteristics of Rule-Based Classifier<br />Mutually exclusive rules<br />Classifier contains mutually exclusive rules if the rules are independent of each other<br />Every record is covered by at most one rule<br />Exhaustive rules<br />Classifier has exhaustive coverage if it accounts for every possible combination of attribute values<br />Each record is covered by at least one rule<br />
- 4. Building Classification Rules<br />Direct Method: <br /> Extract rules directly from data<br /> e.g.: RIPPER, CN2, Holte’s 1R<br />Indirect Method:<br /> Extract rules from other classification models (e.g. decision trees, neural networks, etc).<br />e.g: C4.5rules<br />
- 5. Direct Method: Sequential Covering<br />Start from an empty rule<br />Grow a rule using the Learn-One-Rule function<br />Remove training records covered by the rule<br />Repeat Step (2) and (3) until stopping criterion is met <br />
- 6. Aspects of Sequential Covering<br />Rule Growing<br />Instance Elimination<br />Rule Evaluation<br />Stopping Criterion<br />Rule Pruning<br />
- 7. Contd…<br />Grow a single rule<br />Remove Instances from rule<br />Prune the rule (if necessary)<br />Add rule to Current Rule Set<br />Repeat<br />
- 8. Indirect Method: C4.5rules<br />Extract rules from an unpruned decision tree<br />For each rule, r: A y, <br />consider an alternative rule r’: A’ y where A’ is obtained by removing one of the conjuncts in A<br />Compare the pessimistic error rate for r against all r’s<br />Prune if one of the r’s has lower pessimistic error rate<br />Repeat until we can no longer improve generalization error<br />
- 9. Indirect Method: C4.5rules<br />Instead of ordering the rules, order subsets of rules (class ordering)<br />Each subset is a collection of rules with the same rule consequent (class)<br />Compute description length of each subset<br /> Description length = L(error) + g L(model)<br /> g is a parameter that takes into account the presence of redundant attributes in a rule set (default value = 0.5)<br />
- 10. Advantages of Rule-Based Classifiers<br />As highly expressive as decision trees<br />Easy to interpret<br />Easy to generate<br />Can classify new instances rapidly<br />Performance comparable to decision trees<br />
- 11. Nearest Neighbor Classifiers<br /><ul><li>Requires three things
- 12. The set of stored records
- 13. Distance Metric to compute distance between records
- 14. The value of k, the number of nearest neighbors to retrieve
- 15. To classify an unknown record:
- 16. Compute distance to other training records
- 17. Identify k nearest neighbors
- 18. Use class labels of nearest neighbors to determine the class label of unknown record (e.g., by taking majority vote</li></li></ul><li>Definition of Nearest Neighbor<br />K-nearest neighbors of a record x are data points that have the k smallest distance to x<br />
- 19. Nearest Neighbor Classification…<br />Choosing the value of k:<br />If k is too small, sensitive to noise points<br />If k is too large, neighborhood may include points from other classes<br />Scaling issues<br />Attributes may have to be scaled to prevent distance measures from being dominated by one of the attributes<br />Example:<br /> height of a person may vary from 1.5m to 1.8m<br /> weight of a person may vary from 90lb to 300lb<br />
- 20. Nearest neighbor Classification…<br />k-NN classifiers are lazy learners <br />It does not build models explicitly<br />Unlike eager learners such as decision tree induction and rule-based systems<br />Classifying unknown records are relatively expensive<br />
- 21. Bayes Classifier<br />A probabilistic framework for solving classification problems<br />Conditional Probability:<br />Bayes theorem:<br />
- 22. Example of Bayes Theorem<br />Given: <br />A doctor knows that meningitis causes stiff neck 50% of the time<br />Prior probability of any patient having meningitis is 1/50,000<br />Prior probability of any patient having stiff neck is 1/20<br /> If a patient has stiff neck, what’s the probability he/she has meningitis?<br />
- 23. Naïve Bayes Classifier<br />Assume independence among attributes Ai when class is given: <br />P(A1, A2, …, An |C) = P(A1| Cj) P(A2| Cj)… P(An| Cj)<br />Can estimate P(Ai| Cj) for all Ai and Cj.<br />New point is classified to Cj if P(Cj) P(Ai| Cj) is maximal.<br />
- 24. Naïve Bayes Classifier<br />If one of the conditional probability is zero, then the entire expression becomes zero<br />Probability estimation:<br />c: number of classes<br />p: prior probability<br />m: parameter<br />
- 25. Naïve Bayes (Summary)<br />Robust to isolated noise points<br />Handle missing values by ignoring the instance during probability estimate calculations<br />Robust to irrelevant attributes<br />Independence assumption may not hold for some attributes<br />Use other techniques such as Bayesian Belief Networks (BBN)<br />
- 26. Artificial Neural Networks (ANN)<br />Model is an assembly of inter-connected nodes and weighted links<br />Output node sums up each of its input value according to the weights of its links<br />Compare output node against some threshold t<br />
- 27. General Structure of ANN<br />Training ANN means learning the weights of the neurons<br />
- 28. Algorithm for learning ANN<br />Initialize the weights (w0, w1, …, wk)<br />Adjust the weights in such a way that the output of ANN is consistent with class labels of training examples<br />Objective function:<br />Find the weights wi’s that minimize the above objective function<br /> e.g., backpropagation algorithm <br />
- 29. Ensemble Methods<br />Construct a set of classifiers from the training data<br />Predict class label of previously unseen records by aggregating predictions made by multiple classifiers<br />
- 30. General Idea<br />
- 31. Why does it work?<br />Suppose there are 25 base classifiers<br />Each classifier has error rate, = 0.35<br />Assume classifiers are independent<br />Probability that the ensemble classifier makes a wrong prediction:<br />
- 32. Examples of Ensemble Methods<br />How to generate an ensemble of classifiers?<br />Bagging<br />Boosting<br />
- 33. Bagging<br />Sampling with replacement<br />Build classifier on each bootstrap sample<br />Each sample has probability (1 – 1/n)n of being selected<br />
- 34. Boosting<br />An iterative procedure to adaptively change distribution of training data by focusing more on previously misclassified records<br />Initially, all N records are assigned equal weights<br />Unlike bagging, weights may change at the end of boosting round<br />
- 35. Visit more self help tutorials<br />Pick a tutorial of your choice and browse through it at your own pace.<br />The tutorials section is free, self-guiding and will not involve any additional support.<br />Visit us at www.dataminingtools.net<br />

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