Evaluating Algorithms for Concept Description

When performing concept description, models need to be evaluated both on accuracy and comprehensibility. A comprehensible concept description model should present the most important relationships in the data in an accurate and understandable way. Two natural representations for this are decision trees and decision lists. In this study, the two decision list algorithms RIPPER and Chipper, and the decision tree algorithm C4.5, are evaluated for concept description, using publicly available datasets. The experiments show that C4.5 performs very well regarding accuracy and brevity, i.e. the ability to classify instances with few tests, but also produces large models that are hard to survey and contain many extremely specific rules, thus not being good concept descriptions. The decision list algorithms perform reasonably well on accuracy, and are mostly able to produce small models with relatively good predictive performance. Regarding brevity, Chipper is better than RIPPER, using on average fewer conditions to classify an instance. RIPPER, on the other hand, excels in relevance, i.e. the ability to capture a large number of instances with every rule