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Learning algorithms produce software models for realising criticalclassification tasks. Decision trees models are simpler than other models suchas neural network and they are used in various critical domains such as themedical and the aeronautics. Low or unknown learning ability algorithms doesnot permit us to trust the produced software models, which lead to costly testactivities for validating the models and to the waste of learning time in casethe models are likely to be faulty due to the learning inability. Methods forevaluating the decision trees learning ability, as well as that for the othermodels, are needed especially since the testing of the learned models is stilla hot topic. We propose a novel oracle-centered approach to evaluate (thelearning ability of) learning algorithms for decision trees. It consists ofgenerating data from reference trees playing the role of oracles, producinglearned trees with existing learning algorithms, and determining the degree ofcorrectness (DOE) of the learned trees by comparing them with the oracles. Theaverage DOE is used to estimate the quality of the learning algorithm. the Weassess five decision tree learning algorithms based on the proposed approach.

An Approach to Evaluating Learning Algorithms for Decision Trees