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Multiple-scale cost sensitive decision tree learning

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Abstract

The previous cost-sensitive learning methods assume that the (medical) test cost is measured in the same scale with the misclassification cost while minimizing the expected total cost. This paper proposes a general target-resource framework involving multiple kinds of cost scales, which minimize one kind of cost scale (called target cost scale) through controlling the others (called resource cost scales) in given resource budgets. The proposed cost-sensitive learning model also assists in, such as healthcare data classification and bioinformatics analysis, which are practical and desired application for developing a multiple-scale cost-sensitive learning tool. We experimentally evaluated our approach using the biological and medical datasets, and demonstrated that our proposed method worked well on learning decision tree under a given budget.

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Funding

This work is partially supported by the China Key Research Program (Grant No: 2016YFB1000905); the Natural Science Foundation of China (Grants No: 61573270 and 61672177); the Project of Guangxi Science and Technology (GuiKeAD17195062); the China “1000-Plan” National Distinguished Professorship; the Guangxi Natural Science Foundation (Grant No: 2015GXNSFCB139011); the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing; and the Guangxi “Bagui” Teams for Innovation and Research.

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Shichao Zhang

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  1. Shichao Zhang
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Correspondence to Shichao Zhang.

Additional information

This article belongs to the Topical Collection: Special Issue on Deep Mining Big Social Data

Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell

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Zhang, S. Multiple-scale cost sensitive decision tree learning. World Wide Web 21, 1787–1800 (2018). https://doi.org/10.1007/s11280-018-0619-5

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  • DOI: https://doi.org/10.1007/s11280-018-0619-5

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