Cost-sensitive optimal error bound selection

This paper proposes a cost-sensitive optimal error bound selection approach to address data with measurement errors. The considered misclassification costs are sensitive to both examples and test costs. With this in mind, misclassification costs and test costs are adaptively computed according to er...

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Bibliographic Details
Published inJisuanji Kexue yu Tansuo / Journal of Computer Science and Frontiers Vol. 7; no. 12; pp. 1146 - 1152
Main Authors Lin, Ziqiong, Zhao, Hong
Format Journal Article
LanguageChinese
Published 01.12.2013
Subjects
Algorithms
Computation
Cost engineering
Costs
Error analysis
Errors
Optimization
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Summary:This paper proposes a cost-sensitive optimal error bound selection approach to address data with measurement errors. The considered misclassification costs are sensitive to both examples and test costs. With this in mind, misclassification costs and test costs are adaptively computed according to error bound. In order to minimize average total cost, this paper designs an algorithm for cost-sensitive optimal error bound selection. The experimental results on four UCI databases show that the designed algorithm can select the feature set with the optimal error bound. The selected feature set leads to the lowest average total cost.
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ISSN:1673-9418
DOI:10.3778/j.issn.1673-9418.1307005