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Published on: September 27, 2020
Evaluating learning models with transitions of human interests based on objective rule evaluation indices
Hidenao Abe1, Hideto Yokoi, Shusaku Tsumoto
1Department of Medical Informatics, Shimane University, School of Medicine, Japan. abe@med.shimane-u.ac.jp
Abstract:
This paper presents a method to support the evaluation procedure of a data mining process using human-system interaction. The post-processing of mined results is one of the key factors for successful data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset containing noise. We have designed a method based on objective rule evaluation indices to support the rule evaluation procedure; the indices are calculated to evaluate each if-then rule mathematically. We have evaluated five representative learning algorithms to construct rule evaluation models of the actual data mining results from a chronic hepatitis data set. Further, we discuss the relationship between the transitions of the subjective criterion of a medical expert and the performances of the rule evaluation models.
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