Abstract
Test cost is an important issue in cost-sensitive systems. It is what we pay for obtaining a data item of an object. In some applications, there are some common costs and a cost constraint. The common cost is due to the share of the same resources by several tests. The cost constraint is due to limited money, time, or other resources. Recently, the two issues have been addressed independently in cost-sensitive rough sets. In contrast, this paper considers both issues. Our problem is to construct test sets meeting the constraint and preserving the information of decision systems to the highest degree. We propose a heuristic algorithm to deal with this problem. It is based on information gain, test costs, group-memberships, common costs and a non-positive exponent λ. λ is employed in the penalty function such that expensive tests are unlikely to be chosen. Experimental results indicate that the algorithm performs good in terms of the possibility of finding the optimal reduct. Since the optimal setting of λ is often unknown, we can run the algorithm with different λ values and obtain better results.
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References
Chai, X., Deng, L., Yang, Q., Ling, C.X.: Test-cost sensitive naïve bayes classification. In: ICDM, pp. 51–58 (2004)
He, H., Min, F., Zhu, W.: Attribute reduction in test-cost-sensitive decision systems with common-test-costs. In: ICMLC, vol. 1, pp. 432–436 (2011)
Hu, Q., Yu, D., Liu, J., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection. Information Sciences 178(18), 3577–3594 (2008)
Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Transactions on Knowledge and Data Engineering 16(12), 1457–1471 (2004)
Kukar, M., Kononenko, I.: Cost-sensitive learning with neural networks. In: ECAI, pp. 445–449 (1998)
Min, F., He, H., Qian, Y., Zhu, W.: Test-cost-sensitive attribute reduction. Information Sciences 181, 4928–4942 (2011)
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.: Minimal cost attribute reduction through backtracking. To appear in DTA (2011)
Min, F., Zhu, W.: Optimal sub-reducts in the dynamic environment. To appear in IEEE GrC (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(LNAI), vol. 6954, pp. 57–62. Springer, Heidelberg (2011)
Min, F., Zhu, W., Zhao, H., Pan, G.: Coser: Cost-senstive rough sets (2011), http://grc.fjzs.edu.cn/~fmin/coser/
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
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)
Ślęzak, D.: Approximate entropy reducts. Fundamenta Informaticae 53(3-4), 365–390 (2002)
Turney, P.D.: Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research 2, 369–409 (1995)
Wang, G.: Attribute Core of Decision Table. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 213–217. Springer, Heidelberg (2002)
Yang, Q., Wu, X.: 10 challenging problems in data mining research. International Journal of Information Technology and Decision Making 5(4), 597–604 (2006)
Yao, Y.: A Partition Model of Granular Computing. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 232–253. Springer, Heidelberg (2004)
Yao, Y., Wong, S.: A decision theoretic framework for approximating concepts. International Journal of Man-machine Studies 37, 793–809 (1992)
Yao, Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Information Sciences 178(17), 3356–3373 (2008)
Zhang, W., Mi, J., Wu, W.: Knowledge reductions in inconsistent information systems. Chinese Journal of Computers 26(1), 12–18 (2003)
Zhou, Z., Liu, X.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering 18(1), 63–77 (2006)
Zhu, W.: Topological approaches to covering rough sets. Information Sciences 177(6), 1499–1508 (2007)
Zhu, W., Wang, F.: Reduction and axiomization of covering generalized rough sets. Information Sciences 152(1), 217–230 (2003)
Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46(1), 39–59 (1993)
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Pan, G., Min, F., Zhu, W. (2011). Test Cost Constraint Reduction with Common Cost. In: Kim, Th., et al. Future Generation Information Technology. FGIT 2011. Lecture Notes in Computer Science, vol 7105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27142-7_8
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DOI: https://doi.org/10.1007/978-3-642-27142-7_8
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