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    References

    Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches

    Richard Jensen, Qiang Shen, Pages: 313–336, 2008

    Published Online : 29 JAN 2008, DOI: 10.1002/9780470377888.refs

  2. Evolutionary computation for feature selection in classification problems

    Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

    Volume 3, Issue 6, November/December 2013, Pages: 381–407, Beatriz de la Iglesia

    Version of Record online : 17 OCT 2013, DOI: 10.1002/widm.1106

  3. A variance reduction framework for stable feature selection

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 5, Issue 5, October 2012, Pages: 428–445, Yue Han and Lei Yu

    Version of Record online : 29 JUN 2012, DOI: 10.1002/sam.11152

  4. A NEW E-LEARNING ACHIEVEMENT EVALUATION MODEL BASED ON ROUGH SET AND SIMILARITY FILTER

    Computational Intelligence

    Volume 27, Issue 2, May 2011, Pages: 260–279, Ching-Hsue Cheng, Liang-Ying Wei and Yao-Hsien Chen

    Version of Record online : 5 MAY 2011, DOI: 10.1111/j.1467-8640.2011.00380.x

  5. Revisiting evolutionary algorithms in feature selection and nonfuzzy/fuzzy rule based classification

    Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

    Volume 3, Issue 2, March/April 2013, Pages: 83–108, Satchidananda Dehuri and Ashish Ghosh

    Version of Record online : 20 FEB 2013, DOI: 10.1002/widm.1087

  6. Intraclass and interclass correlation coefficient-based feature selection in NIDS dataset

    Security and Communication Networks

    Volume 8, Issue 18, December 2015, Pages: 3441–3458, Alampallam Ramaswamy Vasudevan and Subramanian Selvakumar

    Version of Record online : 20 MAY 2015, DOI: 10.1002/sec.1269

  7. Discovering Maximal Generalized Decision Rules Through Horizontal and Vertical Data Reduction

    Computational Intelligence

    Volume 17, Issue 4, November 2001, Pages: 685–702, Xiaohua Hu and Nick Cercone

    Version of Record online : 17 DEC 2002, DOI: 10.1111/0824-7935.00169

  8. An exact feature selection algorithm based on rough set theory

    Complexity

    Volume 20, Issue 5, May/June 2015, Pages: 50–62, Mohammad Taghi Rezvan, Ali Zeinal Hamadani and Seyed Reza Hejazi

    Version of Record online : 18 MAR 2014, DOI: 10.1002/cplx.21526

  9. An information theoretic approach for feature selection

    Security and Communication Networks

    Volume 5, Issue 2, February 2012, Pages: 178–185, Gulshan Kumar and Krishan Kumar

    Version of Record online : 2 FEB 2011, DOI: 10.1002/sec.303

  10. Stability of Feature Selection Algorithms and Ensemble Feature Selection Methods in Bioinformatics

    Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data

    Pengyi Yang, Bing B. Zhou, Jean Yee-Hwa Yang, Albert Y. Zomaya, Pages: 333–352, 2013

    Published Online : 27 DEC 2013, DOI: 10.1002/9781118617151.ch14

  11. Granular Computing in Machine Learning and Data Mining

    Handbook of Granular Computing

    Witold Pedrycz, Andrzej Skowron, Vladik Kreinovich, Pages: 889–906, 2008

    Published Online : 16 JUL 2008, DOI: 10.1002/9780470724163.ch42

  12. Rough Set Based Approaches to Feature Selection

    Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches

    Richard Jensen, Qiang Shen, Pages: 85–112, 2008

    Published Online : 29 JAN 2008, DOI: 10.1002/9780470377888.ch5

  13. A Universal neighbourhood rough sets model for knowledge discovering from incomplete heterogeneous data

    Expert Systems

    Volume 30, Issue 1, February 2013, Pages: 89–96, Siyuan Jing, Kun She and Shahzad Ali

    Version of Record online : 2 OCT 2012, DOI: 10.1111/j.1468-0394.2012.00633.x

  14. SELECTING EFFECTIVE FEATURES AND RELATIONS FOR EFFICIENT MULTI-RELATIONAL CLASSIFICATION

    Computational Intelligence

    Volume 26, Issue 3, August 2010, Pages: 258–281, Jun He, Hongyan Liu, Bo Hu, Xiaoyong Du and Puwei Wang

    Version of Record online : 27 JUL 2010, DOI: 10.1111/j.1467-8640.2010.00359.x

  15. Feature selection for clustering categorical data with an embedded modelling approach

    Expert Systems

    Volume 32, Issue 3, June 2015, Pages: 444–453, Cláudia Silvestre, Margarida G. M. S. Cardoso and Mário Figueiredo

    Version of Record online : 23 SEP 2014, DOI: 10.1111/exsy.12082

  16. Feature selection and effective classifiers

    Journal of the American Society for Information Science

    Volume 49, Issue 5, 1998, Pages: 423–434, Jitender S. Deogun, Suresh K. Choubey, Vijay V. Raghavan and Hayri Sever

    Version of Record online : 7 DEC 1998, DOI: 10.1002/(SICI)1097-4571(19980415)49:5<423::AID-ASI5>3.0.CO;2-0

  17. Discriminative frequent subgraph mining with optimality guarantees

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 3, Issue 5, October 2010, Pages: 302–318, Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alex Smola, Le Song, Philip S. Yu, Xifeng Yan and Karsten M. Borgwardt

    Version of Record online : 25 AUG 2010, DOI: 10.1002/sam.10084

  18. Feature Selection and Classification For Gene Expression Data Using Evolutionary Computation

    Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data

    Haider Banka, Suresh Dara, Mourad Elloumi, Pages: 421–440, 2013

    Published Online : 27 DEC 2013, DOI: 10.1002/9781118617151.ch18

  19. Rough-Granular Computing

    Handbook of Granular Computing

    Andrzej Skowron, James F. Peters, Pages: 285–327, 2008

    Published Online : 16 JUL 2008, DOI: 10.1002/9780470724163.ch13

  20. Discrimination Structure Complementarity-Based Feature Selection

    Computational Intelligence

    Shuqin Wang, Jinmao Wei and Zhenglu Yang

    Version of Record online : 4 APR 2017, DOI: 10.1111/coin.12118