On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Extending Dynamic Backtracking for Distributed Constraint Satisfaction Problems
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Repechage Bootstrap Aggregating for Misclassification Cost Reduction
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
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Classification assigns an entity to a category on the basis of feature values encoded from a stimulus. Provided they are presented with sufficient training data, inductive classifier builders such as C4.5 are limited by encoding deficiencies and noise in the data, rather than by the method of deciding the category. However, such classification techniques do not perform well on the small, dirty/or and dynamic data sets which are all that are available in many decision making domains. Moreover, their computational overhead may not be justified. This paper draws on conjectures about human categorization processes to design a frugal algorithm for use with such data. On presentation of an observation, case-specific rules are derived from a small subset of the stored examples, where the subset is selected on the basis of similarity to the encoded stimulus. Attention is focused on those features that appear to be most useful for distinguishing categories of observations similar to the current one. A measure of logical semantic information value is used to discriminate between categories that remain plausible after this. The new classifier is demonstrated against neural net and decision tree classifiers on some standard UCI data sets and shown to perform well.