Paper: A support for decision-making: Cost-sensitive learning system

Authors:
Ivan Bruha;Sylva Koková
Affiliations:
McMaster University, Department of Computer Science and Systems, hamilton, Ont., Canada L8S4K1;Institute of Computer Science AS CR, 18207 Praha, Czech Republic
Venue:
Artificial Intelligence in Medicine
Year:
1994

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Abstract

This paper investigates a machine learning (ML) algorithm for supporting a decisionmaking system that is able to handle diagnostic problems. The input data are expressed by solved cases of patients' diagnoses, and the output is formed by a set of decision rules which may be directly exploited for a decision support. We have chosen the methodology of covering ML algorithms, namely the CN2 algorithm, as a starting point, and designed and implemented a certain extension of CN2 that comprises: advanced discretizing numerical attributes and incorporating attribute cost to economize the classification.