Decision tree classifiers sensitive to heterogeneous costs

Abstract

There are nine major types of cost involved in cost-sensitive learning that are with heterogeneous units in general, referred to heterogeneous costs. Extant cost-sensitive learning (CSL) algorithms are based on the assumption that all involved costs can be transformed into a unified unit, called as homogeneous assumption of costs. While it is a challenge to construct many suitable transformation functions for the costs with diverse units, this paper designs a heterogeneous-cost sensitive learning (HCSL) algorithm to make split attribute selection more effective. This paper first proposes an efficient method of reducing the heterogeneity caused by both cost mechanisms and attribute information. And then, all heterogeneous costs with attribution information together are incorporated into the process of split attribute selection, called as HCAI-based split attribute selection. Third, the over-fitting is tackled by designing a simple and effective smoothing strategy, so as to build cost-sensitive decision tree classifiers with the HCSL algorithm. Experiments are conducted to evaluate the proposed HCSL algorithm on six UCI datasets. Experimental results show that the proposed approach outperforms existing methods for handling the heterogeneity caused by cost mechanisms and attribute information.