Learning from inconsistent and noisy data: The AQ18 approach

In concept learning or data mining tasks, the learner is typically faced with a choice of many possible hypotheses characterizing the data. If one can assume that the training data are nois e-free, then the generated hypothesis should be complete and consistent with regard to the data. In real -world problems, however, data are often noisy, and an insistence on full completeness and consistency is no longer valid. The proble m then is to determine a hypothesis that represents the "best" trade -off between completeness and consistency. This paper presents an approach to this problem in which a learner seeks rules optimizing a description quality criterion that combines completeness and consistency gain , a measure based on consistency that reflects the rule 's benefit . The method has been implemented in the AQ18 learning and data mining system and compared to several other methods. Experiments have indicated the flexibility and power of the proposed method.