Abstract
Test cost is often required to obtain feature values of an object. When this issue is involved, people are often interested in schemes minimizing it. In many data mining applications, due to economic, technological and legal reasons, it is neither possible nor necessary to obtain a classifier with 100% accuracy. There may be an industrial standard to indicate the accuracy of the classification. In this paper, we consider such a situation and propose a new constraint satisfaction problem to address it. The constraint is expressed by the positive region; whereas the objective is to minimize the total test cost. The new problem is essentially a dual of the test cost constraint attribute reduction problem, which has been addressed recently. We propose a heuristic algorithm based on the information gain, the test cost, and a user specified parameter λ to deal with the new problem. Experimental results indicate the rational setting of λ is different among datasets, and the algorithm is especially stable when the test cost is subject to the Pareto distribution.
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References
Chen, X.: An Improved Branch and Bound Algorithm for Feature Selection. Pattern Recognition Letters 24(12), 1925–1933
Greco, S., Matarazzo, B., Słowiński, R., Stefanowski, J.: Variable Consistency Model of Dominance-Based Rough Sets Approach. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 170–181. Springer, Heidelberg (2001)
Hu, Q., Yu, D., Liu, J., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection. Information Sciences 178(18), 3577–3594 (2008)
Min, F., He, H., Qian, Y., Zhu, W.: Test-cost-sensitive attribute reduction. Information Sciences 181, 4928–4942 (2011)
Min, F., Hu, Q., Zhu, W.: Feature selection with test cost constraint. Submitted to Knowledge-Based Systems (2012)
Min, F., Liu, Q.: A hierarchical model for test-cost-sensitive decision systems. Information Sciences 179, 2442–2452 (2009)
Min, F., Zhu, W.: Attribute reduction with test cost constraint. Journal of Electronic Science and Technology of China 9(2), 97–102 (2011)
Min, F., Zhu, W.: Optimal sub-reducts in the dynamic environment. In: IEEE GrC, pp. 457–462 (2011)
Min, F., Zhu, W.: Optimal Sub-Reducts with Test Cost Constraint. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 57–62. Springer, Heidelberg (2011)
Min, F., Zhu, W., Zhao, H., Pan, G., Liu, J.: Coser: Cost-senstive rough sets (2011), http://grc.fjzs.edu.cn/~fmin/coser/
Dash, M., Liu, H.: Feature Selection for Classification. Intelligent Data Analysis 1, 131–156 (1997)
Pawlak, Z.: Rough set approach to knowledge-based decision support. European Joumal of Operational Research 99, 48–57 (1997)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Pawlak, Z.: Rough sets and intelligent data analysis. Information Sciences 147(12), 1–12 (2002)
Qian, Y., Liang, J., Pedrycz, W., Dang, C.: Positive approximation: An accelerator for attribute reduction in rough set theory. Artificial Intelligence 174(9-10), 597–618 (2010)
Swiniarski, W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24(6), 833–849 (2003)
Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters 28(4), 459–471 (2007)
Yao, J., Zhang, M.: Feature Selection with Adjustable Criteria. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 204–213. Springer, Heidelberg (2005)
Yao, Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Information Sciences 178(17), 3356–3373 (2008)
Zhange, W., Mi, J., Wu, W.: Knowledge reductions in inconsistent information systems. Chinese Journal of Computers 26(1), 12–18 (2003)
Zhu, W.: Topological approaches to covering rough sets. Information Sciences 177(6), 1499–1508 (2007)
Zhu, W.: Relationship between generalized rough sets based on binary relation and covering. Information Sciences 179(3), 210–225 (2009)
Zhu, W., Wang, F.: Reduction and axiomization of covering generalized rough sets. Information Sciences 152(1), 217–230 (2003)
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Liu, J., Min, F., Liao, S., Zhu, W. (2012). Minimal Test Cost Feature Selection with Positive Region Constraint. In: Yao, J., et al. Rough Sets and Current Trends in Computing. RSCTC 2012. Lecture Notes in Computer Science(), vol 7413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32115-3_31
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DOI: https://doi.org/10.1007/978-3-642-32115-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32114-6
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