Feature selection with time cost constraint

被引：0

作者：

Sun, Zhongyi ^{[1
]}

Ding, Haijun ^{[1
]}

Zhu, William ^{[1
,2
]}

Zhu, Xiaozhong ^{[1
]}

机构：

[1] College of IOT Engineering, Hohai University

[2] Lab. of Granular Computing, Zhangzhou Normal University

来源：

Journal of Information and Computational Science | 2014年 / 11卷 / 01期

关键词：

Conditional entropy; Constraint; Feature selection; Testing time cost; Waiting cost;

D O I：

10.12733/jics20102664

中图分类号：

学科分类号：

摘要：

Feature selection is an important task in machine learning and data mining. In real world applications, time and money are required to obtain features of objects. Many existing works have been developed to preserve enough information of a decision system, and at the same time, minimize time cost or money cost. In this paper, we study feature selection with time cost constraint. Here time cost consists of testing time cost and waiting cost. The optimization objective is to obtain a feature subset with the lowest conditional informational entropy. Two algorithms are designed to obtain optimal solutions for the problem. We revise the first algorithm by utilizing the relationship of the conditional entropy between a set and its subsets, then obtain the second algorithm. Experimental results on four UCI datasets indicate that the efficiency improvement is significant. 1548-7741/Copyright © 2014 Binary Information Press.

引用

页码：201 / 210

页数：9

共 19 条

[1]

Bae C., Yeh W.C., Chung Y.Y., Liu S.L., Feature selection with intelligent dynamic swarm and rough set, Expert Systems with Applications, 37, 10, pp. 7026-7032, (2010)

[2]

Dash M., Liu H., Feature selection for classification, Intelligent Data Analysis, 1, 1-4, pp. 131-156, (1997)

[3]

Fan H.L., Zhong Y.C., A rough set approach to feature selection based on wasp swarm optimization, Journal of Computational Information Systems, 8, 3, pp. 1037-1045, (2012)

[4]

Sun L., Xu J.C., Li S.Q., Cao X.Z., Gao Y.P., New approach for feature selection by using information entropy, Journal of Information and Computational Science, 8, 12, pp. 2259-2268, (2011)

[5]

Sun Y.X., Lou X.J., Bao B.S., A novel relief feature selection algorithm based on mean-variance model, Journal of Information and Computational Science, 8, 16, pp. 3921-3929, (2011)

[6]

Dai J.H., Heuristic genetic algorithm for minimal reduction decision system based on rough set theory, Proceedings of 2002 International Conference on Machine Learning and Cybernetics, 2, 2002, pp. 833-836

[7]

Min F., He H.P., Qian Y.H., Zhu W., Test-cost-sensitive attribute reduction, Information Sciences, 181, 2011, pp. 4928-4942

[8]

Turney P.D., Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm, Journal of Artificial Intelligence Research, 2, 1995, pp. 369-409

[9]

Min F., Bai Z.J., He M.Y., Liu Q., The reduct problem with specified attributes, Proceedings of Rough Sets and Soft Computing in Intelligent Agent and Web Technology, pp. 36-42, (2005)

[10]

Min F., Du X.H., Qiu H., Liu Q., Minimal attribute space bias for attribute reduction, Proceedings of Rough Set and Knowledge Technology, pp. 379-386, (2007)

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