By Piotr Wasilewski, Andrzej Skowron (auth.), Tianrui Li, Hung Son Nguyen, Guoyin Wang, Jerzy Grzymala-Busse, Ryszard Janicki, Aboul Ella Hassanien, Hong Yu (eds.)
This publication constitutes the refereed lawsuits of the seventh foreign convention on tough units and information know-how, RSKT 2012, held in Chengdu, China in the course of August 2012, as one of many co-located meetings of the 2012 Joint tough Set Symposium, JRS 2012. The sixty three revised papers (including forty two general and 21 brief papers) have been rigorously reviewed and chosen from various submissions. The papers are equipped in topical sections on tough units and its generalizations, tough units in facts and data processing, wisdom expertise, advances in granular computing (AGC 2012 workshop), decision-theoretic tough set version and functions (special session), clever determination making and granular computing (special session), tough set foundations (special session).
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Extra resources for Rough Sets and Knowledge Technology: 7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012. Proceedings
Sample text
The repeated trails help to understand the behavior of the algorithm. The description of the terms given here is applicable to all tables. In reporting SGIQRA IG results Table 1 contains for each sample size, AL (Length of the recommended reduct giving minimum and maximum), IC (Iteration Count: Number of iterations for a trial given are minimum and maximum), CT (Computational Time in seconds, given are the mean(standard deviation)), EC(Excess Count: Number of Redundant attributes found in the recommended reduct.
5%, 1% also on first two datasets. For each sample size the experiment is repeated for 100 trails for all datasets. The repeated trails help to understand the behavior of the algorithm. The description of the terms given here is applicable to all tables. In reporting SGIQRA IG results Table 1 contains for each sample size, AL (Length of the recommended reduct giving minimum and maximum), IC (Iteration Count: Number of iterations for a trial given are minimum and maximum), CT (Computational Time in seconds, given are the mean(standard deviation)), EC(Excess Count: Number of Redundant attributes found in the recommended reduct.
Rough set based featureselection: A review. , Lingras, P. ) Rough Computing: Theories, Technologies and Applications. IGI global (2007) 7. : Boolean Reasoning for Feature Extraction Problems. , Skowron, A. ) ISMIS 1997. LNCS, vol. 1325, pp. 117–126. Springer, Heidelberg (1997) 8. : Rough Set-Aided Keyword Reduction for Text Categorization. Applied Artificial Intelligence 15(9), 843–873 (2001) 9. : IQuickReduct: An Improvement to Quick Reduct Algorithm. , Zhu, W. ) RSFDGrC 2009. LNCS (LNAI), vol.
