Qualitative test-cost sensitive classification

Abstract

a b s t r a c tThis paper reports a new framework for test-cost sensitive classification. It introduces a new loss function definition, in which misclassification cost and cost of feature extraction are combined qualitatively and the loss is conditioned with current and estimated decisions as well as their consistency. This loss function definition is motivated with the following issues. First, for many applications, the relation between different types of costs can be expressed roughly and usually only in terms of ordinal relations, but not as a precise quantitative number. Second, the redundancy between features can be used to decrease the cost; it is possible not to consider a new feature if it is consistent with the existing ones. In this paper, we show the feasibility of the proposed framework for medical diagnosis problems. Our experiments demonstrate that this framework is efficient to significantly decrease feature extraction cost without decreasing accuracy.

Probabilistic personalised cascade with abstention

“…Some recent publications proposed solutions to this problem discussing possible scenarios to estimate regions where a classifier is confident in its decision, i.e., they considered how to learn a rejector efficiently [2][3][4] . An interesting research direction is to explore feature redundancies to reduce the cost of learning and prediction [5] , and to explore structure in cost sensitive learning, using, e.g., Bayesian networks [6] .…”

Isometric Cost-Sensitive Laplacian Eigenmaps for Imbalance Radar Target Recognition

“…Moreover, some binary-class classification algorithm will face up the imbalance data problem when they are generalized to multi-class classification algorithm by using one-vs-rest technique [3]. Actually, the imbalance data classification problem appears frequently in the field of medical diagnosis [4], financial fraud [5], identification [6], and so on. In this paper, we only consider the imbalance data classification problem in radar target recognition.…”

“…Cost-sensitive learning [4][5][6][7][8][9][10][11][12][13] is an effective method to deal with the imbalance data classification problem. In recent year, cost-sensitive learning has been studied widely and become one of the most important topics in the field of machine learning.…”