Paper Info
Title
A simple methodology for soft cost-sensitive classification
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.
Year
DOI
Venue
2012
10.1145/2339530.2339555
KDD
Keywords
Field
DocType
cost-sensitive classification algorithm,novel cost-sensitive classification methodology,error rate,soft cost-sensitive classification,cost-sensitive classification,simple methodology,lower test error rate,classification error,high error rate,proposed methodology,classification,regularization,multicriteria optimization
Data mining,Data set,One-class classification,Computer science,Soft Costs,Word error rate,Multi-objective optimization,Regularization (mathematics),Artificial intelligence,Statistical classification,Bayes error rate,Machine learning
Conference
Citations 
PageRank 
References 
7
0.47
25
Authors
4
Name
Order
Citations
PageRank
Te-Kang Jan1121.28
Da-Wei Wang2395.93
Chi-Hung Lin321734.67
Hsuan-Tien Lin482974.77