Evaluating learning algorithms composed by a constructive meta-learning scheme for a rule evaluation support method

The Post-processing of mined patterns such as rules, trees and so forth is one of the key operations in a data mining process. However, it is difficult for human experts to completely evaluate several thousand rules from a large dataset with noise. To reduce the cost of this kind of rule evaluation task, we have developed a rule evaluation support method with rule evaluation models that learn from objective indices for mined classification rules and evaluations by a human expert for each rule. In this paper, we present an evaluation of the learning algorithms of our rule evaluation support method for the post-processing of mined patterns with rule evaluation models based on objective indices. To enhance the adaptability of these rule evaluation models, we introduced a constructive meta-learning system for the construction of appropriate learning algorithms. We then have performed case studies using the meningitis as an actual problem. Furthermore, we evaluated our method with the eight rule sets obtained from eight UCI datasets. With regard to these results, we show the applicability of the constrictive meta-learning scheme as a learning algorithm selection method for our rule evaluation support method.