Transactions on Rough Sets XVIII by James F. Peters, Andrzej Skowron, Tianrui Li, Yan Yang,

By James F. Peters, Andrzej Skowron, Tianrui Li, Yan Yang, JingTao Yao, Hung Son Nguyen

The LNCS magazine Transactions on tough units is dedicated to the full spectrum of tough units comparable concerns, from logical and mathematical foundations, via all elements of tough set conception and its purposes, resembling facts mining, wisdom discovery, and clever details processing, to kinfolk among tough units and different techniques to uncertainty, vagueness, and incompleteness, comparable to fuzzy units and thought of evidence.

Volume XVIII comprises extensions of papers from the Joint tough Set Symposium (JRS 2012), which used to be held in Chengdu, China, in August 2012. The seven papers that represent this quantity care for issues resembling: tough fuzzy units, intuitionistic fuzzy units, multi-granulation tough units, decision-theoretic tough units, three-way judgements and their functions in characteristic relief, characteristic choice, overlapping clustering, info mining, cost-sensitive studying, face reputation, and unsolicited mail filtering.

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Sci. 178(17), 3356–3373 (2008) Feature Selection with Positive Region Constraint 33 38. : Knowledge reductions in inconsistent information systems. Chin. J. Comput. 26(1), 12–18 (2003) 39. : A backtracking approach to minimal cost feature selection of numerical data. J. Inf. Comput. Sci. 10(13), 4105–4115 (2013) 40. : Test-cost-sensitive attribute reduction based on neighborhood rough set. In: Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 802–806 (2011) 41. : Test-cost-sensitive attribute reduction of data with normal distribution measurement errors.

174(9–10), 597–618 (2010) 30. : The discernibility matrices and functions in information systems. In: Intelligent Decision Support (1992) 31. : Rough set methods in feature selection and recognition. Pattern Recogn. Lett. 24(6), 833–849 (2003) 32. : Rough set-based approach to feature selection in customer relationship management. Omega 35(4), 365–383 (2007) 33. : Attribute core of decision table. , Zhong, N. ) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 213–217. Springer, Heidelberg (2002) 34. : Feature selection based on rough sets and particle swarm optimization.

The minimal test cost reduct problem is in turn more general than the classical reduct problem, which is NP-hard. Therefore the FSPRC problem is at least NP-hard, and heuristic algorithms are needed to deal with it. Note that the FSPRC is different with the variable precision rough set model. The variable precision rough set model changes the lower approximation by varying the accuracy, but in our problem definition, it is unchanged. 3 The Algorithm Similar to the heuristic algorithm to the OSRT problem [20], we also design a heuristic algorithm to deal with the new problem.

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