A method to use uncertain domain knowledge in the induction of classification knowledge based on ID3

Abstract

We propose a method to use uncertain and qualitative domain knowledge in inducing a classification tree based on ID3. We introduce a consistency degree between data and domain knowledge such as “The reduction rate in the latter stage is larger, the quality of a product is usually the better.” As criteria for inducing a decision tree, we use the consistency degree together with the traditional criterion based on the information-theoretic measure. In this work, the consistency degree is mainly used for pruning the hypotheses whose consistency degree with domain knowledge is below a pre-specified threshold. We demonstrate the effectiveness of our method using the data in a superconducting wire manufacturing domain.