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Author Wang, Da-Wei ♦ Lin, Chi-Hung ♦ Lin, Hsuan-Tien
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Description in Proc. SIGKDD, 2012
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 min-imizing 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 multi-criteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classifica-tion with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classifi-cation algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology in-deed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification al-gorithms.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article