Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

1. Front Matter

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2. Invited Papers

   1. Generalizing Rough Set Theory Through Dominance-Based Rough Set Approach

      • Salvatore Greco, Benedetto Matarazzo, Roman Słowiński

      Pages 1-11

   2. Approximate Boolean Reasoning Approach to Rough Sets and Data Mining

      • Hung Son Nguyen

      Pages 12-22

   3. Towards Human-Level Web Intelligence

      • Ning Zhong

      Pages 23-28

3. Rough Set Software

   1. Credibility Coefficients in ARES Rough Set Exploration System

      • Roman Podraza, Mariusz Walkiewicz, Andrzej Dominik

      Pages 29-38

   2. DIXER – Distributed Executor for Rough Set Exploration System

      • Jan G. Bazan, Rafał Latkowski, Marcin Szczuka

      Pages 39-47

   3. \({\mathcal R}o{\mathcal S}y\): A Rough Knowledge Base System

      • Robin Andersson, Aida Vitória, Jan Małuszyński, Jan Komorowski

      Pages 48-58

4. Data Mining

   1. A Classification Model: Syntax and Semantics for Classification

      • Anita Wasilewska, Ernestina Menasalvas

      Pages 59-68

   2. “Rule + Exception” Strategies for Knowledge Management and Discovery

      • Yiyu Yao, Fei-Yue Wang, Jue Wang

      Pages 69-78

   3. Outlier Detection Using Rough Set Theory

      • Feng Jiang, Yuefei Sui, Cungen Cao

      Pages 79-87

   4. Reverse Prediction

      • Julia Johnson, Patrick Campeau

      Pages 88-97

   5. Prediction Mining – An Approach to Mining Association Rules for Prediction

      • Jitender Deogun, Liying Jiang

      Pages 98-108

   6. A Rough Set Based Model to Rank the Importance of Association Rules

      • Jiye Li, Nick Cercone

      Pages 109-118

5. Hybrid and Hierarchical Methods

   1. A Hierarchical Approach to Multimodal Classification

      • Andrzej Skowron, Hui Wang, Arkadiusz Wojna, Jan Bazan

      Pages 119-127

   2. Rough Learning Vector Quantization Case Generation for CBR Classifiers

      • Yan Li, Simon Chi-Keung Shiu, Sankar Kumar Pal, James Nga-Kwok Liu

      Pages 128-137

   3. ML-CIDIM: Multiple Layers of Multiple Classifier Systems Based on CIDIM

      • Gonzalo Ramos-Jiménez, José del Campo-Ávila, Rafael Morales-Bueno

      Pages 138-146

   4. Constructing Rough Decision Forests

      • Qing-Hua Hu, Da-Ren Yu, Ming-Yang Wang

      Pages 147-156

   5. Attribute Reduction in Concept Lattice Based on Discernibility Matrix

      • Wen-Xiu Zhang, Ling Wei, Jian-Jun Qi

      Pages 157-165

   6. Reducing the Storage Requirements of 1-v-1 Support Vector Machine Multi-classifiers

      • Pawan Lingras, Cory J. Butz

      Pages 166-173

   7. A Possibilistic Approach to RBFN Centers Initialization

      • A. Guillén, I. Rojas, J. González, H. Pomares, L. J. Herrera, O. Valenzuela et al.

      Pages 174-183

This volume contains the papers selected for presentation at the 10th Int- national Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005, organized at the University of Regina, August 31st–September 3rd, 2005. This conference followed in the footsteps of inter- tional events devoted to the subject of rough sets, held so far in Canada, China, Japan,Poland,Sweden, and the USA. RSFDGrC achievedthe status of biennial international conference, starting from 2003 in Chongqing, China. The theory of rough sets, proposed by Zdzis law Pawlak in 1982, is a model of approximate reasoning. The main idea is based on indiscernibility relations that describe indistinguishability of objects. Concepts are represented by - proximations. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to signi?cant results in many areas such as ?nance, industry, multimedia, and medicine. The RSFDGrC conferences put an emphasis on connections between rough sets and fuzzy sets, granularcomputing, and knowledge discoveryand data m- ing, both at the level of theoretical foundations and real-life applications. In the case of this event, additional e?ort was made to establish a linkage towards a broader range of applications. We achieved it by including in the conference program the workshops on bioinformatics, security engineering, and embedded systems, as well as tutorials and sessions related to other application areas.

• artificial intelligence
• cognition
• data mining
• evolution
• evolutionary computation
• fuzzy
• information system
• learning
• machine learning
• multimedia
• probabilistic network
• uncertain reasoning

• Department of Computer Science, University of Regina, Regina, SK, S4S 0A2 Canada, Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warsaw, P.O. Box, Poland

  Dominik Ślęzak

• Department of Computer Science, University of Regina, Regina, Canada

  JingTao Yao, Wojciech Ziarko

• Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada

  James F. Peters

• College of Computer and Information Engineering, Hehan University, Henan, China

  Xiaohua Hu

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