Rough Sets and Intelligent Systems Paradigms

1. Front Matter

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2. Plenary Lecture and Tutorial Papers

   1. The Impact of Local Data Characteristics on Learning from Imbalanced Data

      • Jerzy Stefanowski

      Pages 1-13

   2. The Relational Construction of Conceptual Patterns - Tools, Implementation and Theory

      • Piero Pagliani

      Pages 14-27

   3. SQL-Based KDD with Infobright’s RDBMS: Attributes, Reducts, Trees

      • Jakub Wróblewski, Sebastian Stawicki

      Pages 28-41

3. Foundations of Rough Set Theory

   1. From Vagueness to Rough Sets in Partial Approximation Spaces

      • Zoltán Ernő Csajbók, Tamás Mihálydeák

      Pages 42-52

   2. Random Probes in Computation and Assessment of Approximate Reducts

      • Andrzej Janusz, Dominik Ślęzak

      Pages 53-64

   3. Applications of Boolean Kernels in Rough Sets

      • Sinh Hoa Nguyen, Hung Son Nguyen

      Pages 65-76

   4. Robust Ordinal Regression for Dominance-Based Rough Set Approach under Uncertainty

      • Roman Słowiński, Miłosz Kadziński, Salvatore Greco

      Pages 77-87

   5. Monotonic Uncertainty Measures in Probabilistic Rough Set Model

      • Guoyin Wang, Xi’ao Ma, Hong Yu

      Pages 88-98

   6. Attribute Subset Quality Functions over a Universe of Weighted Objects

      • Sebastian Widz, Dominik Ślęzak

      Pages 99-110

   7. A Definition of Structured Rough Set Approximations

      • Yiyu Yao, Mengjun Hu

      Pages 111-122

4. Granular Computing and Covering-Based Rough Sets

   1. Interactive Computations on Complex Granules

      • Andrzej Jankowski, Andrzej Skowron, Roman Swiniarski

      Pages 123-134

   2. Formulation and Simplification of Multi-Granulation Covering Rough Sets

      • Tong-Jun Li, Xing-Xing Zhao, Wei-Zhi Wu

      Pages 135-142

   3. Covering Based Rough Sets and Relation Based Rough Sets

      • Mauricio Restrepo, Jonatan Gómez

      Pages 143-152

5. Applications of Rough Sets

   1. A Rough Set Approach to Novel Compounds Activity Prediction Based on Surface Active Properties and Molecular Descriptors

      • Jerzy Błaszczyński, Łukasz Pałkowski, Andrzej Skrzypczak, Jan Błaszczak, Alicja Nowaczyk, Roman Słowiński et al.

      Pages 153-160

   2. Rough Sets in Ortholog Gene Detection

      • Deborah Galpert Cańizares, Reinier Millo Sánchez, María Matilde García Lorenzo, Gladys Casas Cardoso, Ricardo Grau Abalo, Leticia Arco García

      Pages 161-168

   3. Hybrid Model Based on Rough Sets Theory and Fuzzy Cognitive Maps for Decision-Making

      • Gonzalo Nápoles, Isel Grau, Koen Vanhoof, Rafael Bello

      Pages 169-178

   4. Decision Rules-Based Probabilistic MCDM Evaluation Method – An Empirical Case from Semiconductor Industry

      • Kao-Yi Shen, Gwo-Hshiung Tzeng

      Pages 179-190

6. Induction of Decision Rules - Theory and Practice

   1. Decision Rule Classifiers for Multi-label Decision Tables

      • Fawaz Alsolami, Mohammad Azad, Igor Chikalov, Mikhail Moshkov

      Pages 191-197

   2. Considerations on Rule Induction Procedures by STRIM and Their Relationship to VPRS

      • Yuichi Kato, Tetsuro Saeki, Shoutarou Mizuno

      Pages 198-208

This book constitutes the refereed proceedings of the 23rd Australasian Joint Conference on Rough Sets and Intelligent Systems Paradigms, RSEISP 2014, held in Granada and Madrid, Spain, in July 2014. RSEISP 2014 was held along with the 9th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2014, as a major part of the 2014 Joint Rough Set Symposium, JRS 2014. JRS 2014 received 40 revised full papers and 37 revised short papers which were carefully reviewed and selected from 120 submissions and presented in two volumes. This volume contains the papers accepted for the conference RSEISP 2014, as well as the three invited papers presented at the conference. The papers are organized in topical sections on plenary lecture and tutorial papers; foundations of rough set theory; granular computing and covering-based rough sets; applications of rough sets; induction of decision rules - theory and practice; knowledge discovery; spatial data analysis and spatial databases; information extraction from images.

• SVM
• approximate dynamic programming methods
• artificial intelligence
• data mining
• decision analysis
• decision analysis
• feature selection
• hybrid systems
• neural networks
• reliability
• support vector machines
• virtual reality
• virtual reality
• visualization

• Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

  Marzena Kryszkiewicz, Zbigniew W. Raś

• Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

  Chris Cornelis

• DISCo, Università di Milano – Bicocca, Milano, Italy

  Davide Ciucci

• Dpt. de Matemáticas, University of Càdiz, Spain

  Jesús Medina-Moreno

• School of Computing and Information Systems, University of Tasmania, Japan

  Hiroshi Motoda

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