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

摘要

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.