A simple methodology for soft cost-sensitive classification

Authors:
Te-Kang Jan;Da-Wei Wang;Chi-Hung Lin;Hsuan-Tien Lin
Affiliations:
Academia Sinica, Taipei, Taiwan Roc;Academia Sinica, Taipei, Taiwan Roc;National Yang-Ming University, Taipei, Taiwan Roc;National Taiwan University, Taipei, Taiwan Roc
Venue:
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2012

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Abstract

Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel cost-sensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms.