Cost sensitive classification in data mining

Authors:
Zhenxing Qin;Chengqi Zhang;Tao Wang;Shichao Zhang
Affiliations:
Faculty of Information Technology, University of Technology, Sydney, Sydney, NSW, Australia;Faculty of Information Technology, University of Technology, Sydney, Sydney, NSW, Australia;Faculty of Information Technology, University of Technology, Sydney, Sydney, NSW, Australia;Faculty of Information Technology, University of Technology, Sydney, Sydney, NSW, Australia
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

Citing 14
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Abstract

Cost-sensitive classification is one of mainstream research topics in data mining and machine learning that induces models from data with unbalance class distributions and impacts by quantifying and tackling the unbalance. Rooted in diagnosis data analysis applications, there are great many techniques developed for cost-sensitive learning. They are mainly focused on minimizing the total cost of misclassification costs, test costs, or other types of cost, or a combination among these costs. This paper introduces the up-to-date prevailing cost-sensitive learning methods and presents some research topics by outlining our two new results: lazy-learning and semi-learning strategies for costsensitive classifiers.