Bidirectional heuristic attribute reduction based on conflict region

Attribute reduction is one of the key issues in rough set theory. Many heuristic reduction strategies such as forward heuristic reduction, backward heuristic reduction and for-backward heuristic reduction have been proposed to obtain a subset of attributes which has the same discernibility as the original attribute set. However, some methods are usually computationally time consuming for large data sets. Therefore, this paper focuses on solving the attribute reduction efficiency in the decision system. We first introduce the quotient of approximation, positive region and conflict region, and then research the heuristic reduction algorithm based on conflict region. Sequentially, we put forward to a mechanism of bidirectional heuristic attribute reduction based on conflict region quotient and design a bidirectional heuristic attribute reduction algorithm. Finally, the experimental results with UCI data sets show that the proposed reduction algorithm is an effective technique to deal with large high-dimensional data sets.

[1]  Guan Lihe.  A New Algorithm for Attribute Reduction Based on Discernibility Matrix , 2007, ICFIE.

[2]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[3]  Huan Liu,et al.  Consistency-based search in feature selection , 2003, Artif. Intell..

[4]  Jiang Yunliang,et al.  Quick attribute reduct algorithm for neighborhood rough set model , 2014 .

[5]  Marzena Kryszkiewicz.  Comparative study of alternative types of knowledge reduction in inconsistent systems , 2001, Int. J. Intell. Syst..

[6]  Qiang Shen,et al.  Rough set-aided keyword reduction for text categorization , 2001, Appl. Artif. Intell..

[7]  Guoyin Wang,et al.  A Comparative Study of Algebra Viewpoint and Information Viewpoint in Attribute Reduction , 2005, Fundam. Informaticae.

[8]  Ivo Düntsch,et al.  Uncertainty Measures of Rough Set Prediction , 1998, Artif. Intell..

[9]  Witold Pedrycz,et al.  An efficient accelerator for attribute reduction from incomplete data in rough set framework , 2011, Pattern Recognit..

[10]  Szymon Jaroszewicz,et al.  Finding reducts without building the discernibility matrix , 2005, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05).

[11]  Guowei Yang,et al.  Knowledge reduction in a new information view , 2005, Proceedings. 2005 International Conference on Communications, Circuits and Systems, 2005..

[12]  Xu Zhang,et al.  A Quick Attribute Reduction Algorithm with Complexity of max(O(｜C｜｜U｜),O(｜C｜~2｜U/C｜)) , 2006 .

[13]  Jiye Liang,et al.  Combination Entropy and Combination Granulation in Rough Set Theory , 2008, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[14]  Miao Duo,et al.  A HEURISTIC ALGORITHM FOR REDUCTION OF KNOWLEDGE , 1999 .

[15]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

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

[17]  Wang Guo,et al.  Decision Table Reduction based on Conditional Information Entropy , 2002 .

[18]  Yiyu Yao,et al.  Discernibility matrix simplification for constructing attribute reducts , 2009, Inf. Sci..

[19]  Shi Zhong-zhi.  Novel Heuristic Algorithm for Knowledge Reduction , 2009 .

[20]  Witold Pedrycz,et al.  Analysis of alternative objective functions for attribute reduction in complete decision tables , 2011, Soft Comput..

[21]  Miao Duo,et al.  An Information Representation of the Concepts and Operations in Rough Set Theory , 1999 .

[22]  Jiye Liang,et al.  The Algorithm on Knowledge Reduction in Incomplete Information Systems , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[23]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[24]  Jiye Liang,et al.  An accelerator for attribute reduction based on perspective of objects and attributes , 2013, Knowl. Based Syst..

[25]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[26]  Wang Ju,et al.  Reduction algorithms based on discernibility matrix: The ordered attributes method , 2001, Journal of Computer Science and Technology.

[27]  Maciej Modrzejewski,et al.  Feature Selection Using Rough Sets Theory , 1993, ECML.

[28]  Liu Shao,et al.  Research on Efficient Algorithms for Rough Set Methods , 2003 .

[29]  Jiye Liang,et al.  International Journal of Approximate Reasoning an Efficient Rough Feature Selection Algorithm with a Multi-granulation View , 2022 .

[30]  Witold Pedrycz,et al.  Positive approximation: An accelerator for attribute reduction in rough set theory , 2010, Artif. Intell..

[31]  Edoardo Amaldi,et al.  On the Approximability of Minimizing Nonzero Variables or Unsatisfied Relations in Linear Systems , 1998, Theor. Comput. Sci..

[32]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[33]  Qiang Shen,et al.  A rough-fuzzy approach for generating classification rules , 2002, Pattern Recognit..

[34]  Long-Shu Li,et al.  An Efficient Attribute Reduction Algorithm Based on Conflict Region: An Efficient Attribute Reduction Algorithm Based on Conflict Region , 2012 .

[35]  Ning Zhong,et al.  Using Rough Sets with Heuristics for Feature Selection , 1999, Journal of Intelligent Information Systems.

[36]  Guoyin Wang,et al.  Quick Knowledge Reduction Based on Divide and Conquer Method in Huge Data Sets , 2007, PReMI.

[37]  Qiang Shen,et al.  A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems , 2000 .

[38]  XIAOHUA Hu,et al.  LEARNING IN RELATIONAL DATABASES: A ROUGH SET APPROACH , 1995, Comput. Intell..

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

[40]  Dominik Slezak,et al.  Approximate Entropy Reducts , 2002, Fundam. Informaticae.

[41]  Wei-Zhi Wu,et al.  Approaches to knowledge reductions in inconsistent systems , 2003, Int. J. Intell. Syst..

[42]  Chu Jian.  Quick Attribute Reduction Algorithm with Hash , 2009 .

[43]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[44]  Andrzej Skowron,et al.  Rough set methods in feature selection and recognition , 2003, Pattern Recognit. Lett..

[45]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[46]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[47]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[48]  Jing Zhang,et al.  A New Heuristic Reduct Algorithm Base on Rough Sets Theory , 2003, WAIM.

[49]  Min Li,et al.  Quick attribute reduction in inconsistent decision tables , 2014, Inf. Sci..

[50]  Yiyu Yao,et al.  Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model , 2009, Inf. Sci..

[51]  W. Li,et al.  Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation , 2011, Int. J. Approx. Reason..

[52]  Zhao Zhang,et al.  Locality preserving multimodal discriminative learning for supervised feature selection , 2011, Knowledge and Information Systems.

[53]  R. Słowiński.  Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .

[54]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[55]  Wang Ju,et al.  DATA ENRICHING BASED ON ROUGH SET THEORY , 1998 .

[56]  Zdzislaw Pawlak,et al.  Rough sets and intelligent data analysis , 2002, Inf. Sci..

[57]  Marzena Kryszkiewicz,et al.  Certain, Generalized Decision, and Membership Distribution Reducts Versus Functional Dependencies in Incomplete Systems , 2007, RSEISP.