NDLI: An iterative method for multi-class cost-sensitive learning

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An iterative method for multi-class cost-sensitive learning

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An iterative method for multi-class cost-sensitive learning

Content Provider	ACM Digital Library
Author	Abe, Naoki Langford, John Zadrozny, Bianca
Abstract	Cost-sensitive learning addresses the issue of classification in the presence of varying costs associated with different types of misclassification. In this paper, we present a method for solving multi-class cost-sensitive learning problems using any binary classification algorithm. This algorithm is derived using hree key ideas: 1) iterative weighting; 2) expanding data space; and 3) gradient boosting with stochastic ensembles. We establish some theoretical guarantees concerning the performance of this method. In particular, we show that a certain variant possesses the boosting property, given a form of weak learning assumption on the component binary classifier. We also empirically evaluate the performance of the proposed method using benchmark data sets and verify that our method generally achieves better results than representative methods for cost-sensitive learning, in terms of predictive performance (cost minimization) and, in many cases, computational efficiency.
Starting Page	3
Ending Page	11
Page Count	9
File Format	PDF
ISBN	1581138881
DOI	10.1145/1014052.1014056
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cost-sensitive learning Multi-class classification Boosting
Content Type	Text
Resource Type	Article

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