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Multi-objective cost-sensitive attribute reduction on data with error ranges

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Abstract

In current supervised machine learning research spectrum, there are several attribute reduction methodologies to acquire reducts with low test cost. They can deal with symbolic data, or numeric data with error ranges. In many cases, they consider the situation with only one type of cost; therefore the problem is single-objective. This paper addresses the attribute reduction problem on data with multi-type-costs and error ranges. First, we define the multi-objective attribute reduction problem where multi-type-costs are involved. Second, we propose three metrics to evaluate the quality of a reduct set. Third, we design a backtrack algorithm to compute the Pareto optimal set, and a heuristic algorithm to find a sub-optimal reduct set. Finally, we compare these algorithms on seven UCI (University of California-Irvine) datasets. Experimental results indicate that our heuristic algorithm has good capability of tackling the proposed problem.

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Acknowledgments

This work is supported in part by the Natural Science Foundation of Sichuan Province Ministry of Education under Grant No. 11ZB018 and 12ZA292, National Science Foundation of China under Grant No. 61379089 and Scientific Research Startingproject of SWPU under Grant No. 2014QHZ025.

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Authors and Affiliations

  1. School of Computer Science, Southwest Petroleum University, Chengdu, 610500, China

    Yu Fang, Zhong-Hui Liu & Fan Min

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  1. Yu Fang
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  2. Zhong-Hui Liu
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  3. Fan Min
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Correspondence to Yu Fang.

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Fang, Y., Liu, ZH. & Min, F. Multi-objective cost-sensitive attribute reduction on data with error ranges. Int. J. Mach. Learn. & Cyber. 7, 783–793 (2016). https://doi.org/10.1007/s13042-014-0296-3

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  • DOI: https://doi.org/10.1007/s13042-014-0296-3

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