A simple methodology for soft cost-sensitive classification

Te-Kang Jan, Da-Wei Wang, Chi-Hung Lin, Hsuan-Tien Lin
2012 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12  
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 costsensitive classification methodology that takes both the cost and the
more » ... ror 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.
doi:10.1145/2339530.2339555 dblp:conf/kdd/JanWLL12 fatcat:vzgmkndv7fbrpmxviflkouv6by