Fuzzy-Rough Data Mining

It is estimated that every 20 months or so the amount of information in the world doubles. In the same way, tools that mine knowledge from data must develop to combat this growth. Fuzzy-rough set theory provides a framework for developing such applications in a way that combines the best properties of fuzzy sets and rough sets, in order to handle uncertainty. In this tutorial we will cover the mathematical groundwork required for an understanding of the data mining methods, before looking at some of the key developments in the area, including feature selection and classifier learning..

[1]  Zahra Shaeiri,et al.  Genetic diagnosis of cancer by fuzzy-rough gene selection and the complementary hierarchical fuzzy classifier. , 2011, Bio-medical materials and engineering.

[2]  Jian Chen,et al.  Short-Term Load Forecasting Model For Power System Based on Complementation of Fuzzy-Rough Set Theory And BP Neural Network , 2007, 2007 IEEE International Conference on Automation and Logistics.

[3]  Urban Ask,et al.  Business Intelligence Practices: Adding Evidence from Organizations in the Nordic Countries , 2013, Int. J. Bus. Intell. Res..

[4]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[5]  Chris Cornelis,et al.  Attribute selection with fuzzy decision reducts , 2010, Inf. Sci..

[6]  Guoyin Wang,et al.  Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing , 2013, Lecture Notes in Computer Science.

[7]  Qiang Shen,et al.  Interval-valued fuzzy-rough feature selection in datasets with missing values , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[8]  Xizhao Wang,et al.  Attributes Reduction Using Fuzzy Rough Sets , 2008, IEEE Transactions on Fuzzy Systems.

[9]  Qiang Shen,et al.  Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring , 2004, Pattern Recognit..

[10]  Didier Dubois,et al.  Putting Rough Sets and Fuzzy Sets Together , 1992, Intelligent Decision Support.

[11]  Andrzej Skowron,et al.  Modeling rough granular computing based on approximation spaces , 2012, Inf. Sci..

[12]  Irene Garrigós,et al.  Business Intelligence Applications and the Web: Models, Systems and Technologies , 2011 .

[13]  Chris Cornelis,et al.  A New Approach to Fuzzy-Rough Nearest Neighbour Classification , 2008, RSCTC.

[14]  Chris Cornelis,et al.  Fuzzy-Rough Nearest Neighbour Classification , 2011, Trans. Rough Sets.

[15]  Francisco Herrera,et al.  Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection , 2012, Inf. Sci..

[16]  Richard Jensen,et al.  QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions. , 2012, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[17]  Sankar K. Pal,et al.  Fuzzy–Rough Sets for Information Measures and Selection of Relevant Genes From Microarray Data , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Fei Luo,et al.  Effluent Quality Prediction of Wastewater Treatment Plant Based on Fuzzy-Rough Sets and Artificial Neural Networks , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[19]  Chris Cornelis,et al.  Fuzzy-rough instance selection , 2010, International Conference on Fuzzy Systems.

[20]  Daniel S. Yeung,et al.  On attributes reduction with fuzzy rough sets , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[21]  Manish Sarkar,et al.  Fuzzy-rough nearest neighbor algorithms in classification , 2007, Fuzzy Sets Syst..

[22]  Qinghua Hu,et al.  Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation , 2007, Pattern Recognit..

[23]  Qinghua Hu,et al.  Soft fuzzy rough sets for robust feature evaluation and selection , 2010, Inf. Sci..

[24]  Qiang Shen,et al.  New Approaches to Fuzzy-Rough Feature Selection , 2009, IEEE Transactions on Fuzzy Systems.

[25]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[26]  Qinghua Hu,et al.  Parameterized attribute reduction with Gaussian kernel based fuzzy rough sets , 2011, Inf. Sci..

[27]  Zhi-Chun Li,et al.  A Method of Fault Diagnosis in Nuclear Plant Based on Fuzzy Sets , 2011, 2011 Second International Conference on Innovations in Bio-inspired Computing and Applications.

[28]  Chris Cornelis,et al.  A noise-tolerant approach to fuzzy-rough feature selection , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[29]  Manish Sarkar,et al.  Fuzzy-rough nearest neighbors algorithm , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[30]  Qiang Shen,et al.  Fuzzy-Rough Feature Significance for Fuzzy Decision Trees , 2005 .

[31]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[32]  Qinghua Hu,et al.  Information-preserving hybrid data reduction based on fuzzy-rough techniques , 2006, Pattern Recognit. Lett..

[33]  Feifei Xu,et al.  Fuzzy-rough attribute reduction via mutual information with an application to cancer classification , 2009, Comput. Math. Appl..

[34]  Qiang Shen,et al.  Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches , 2008, IEEE Press series on computational intelligence.

[35]  Fei Luo,et al.  Wastewater effluent prediction based on fuzzy-rough sets RBF neural networks , 2010, 2010 International Conference on Networking, Sensing and Control (ICNSC).

[36]  De-gang Chen,et al.  On the reduction of fuzzy rough sets , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[37]  Anna Maria Radzikowska,et al.  A comparative study of fuzzy rough sets , 2002, Fuzzy Sets Syst..

[38]  Pritam Ranjan,et al.  A New Tree-based Classifier for Satellite Images , 2013, ArXiv.

[39]  Chris Cornelis,et al.  Ordered Weighted Average Based Fuzzy Rough Sets , 2010, RSKT.

[40]  Andrzej Skowron,et al.  New Directions in Rough Sets, Data Mining, and Granular-Soft Computing , 1999, Lecture Notes in Computer Science.

[41]  Jerzy W. Grzymala-Busse,et al.  Transactions on Rough Sets XIII , 2011, Lecture Notes in Computer Science.

[42]  David M. Steiger,et al.  Decision Support as Knowledge Creation: A Business Intelligence Design Theory , 2010, Int. J. Bus. Intell. Res..

[43]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[44]  Qinghua Hu,et al.  Feature Selection via Maximizing Fuzzy Dependency , 2010, Fundam. Informaticae.

[45]  Wojciech Ziarko,et al.  Decision Making with Probabilistic Decision Tables , 1999, RSFDGrC.

[46]  C. Cornelis,et al.  Vaguely Quantified Rough Sets , 2009, RSFDGrC.

[47]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[48]  Chris Cornelis,et al.  Hybrid fuzzy-rough rule induction and feature selection , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[49]  Qinghua Hu,et al.  A Novel Algorithm for Finding Reducts With Fuzzy Rough Sets , 2012, IEEE Transactions on Fuzzy Systems.

[50]  D. Dubois,et al.  ROUGH FUZZY SETS AND FUZZY ROUGH SETS , 1990 .