An incremental attribute reduction approach based on knowledge granularity under the attribute generalization

Attribute reduction is a key step to discover interesting patterns in the decision system with numbers of attributes available. In recent years, with the fast development of data processing tools, the information system may increase quickly in attributes over time. How to update attribute reducts efficiently under the attribute generalization becomes an important task in knowledge discovery related tasks since the result of attribute reduction may alter with the increase of attributes. This paper aims for investigation of incremental attribute reduction algorithm based on knowledge granularity in the decision system under the variation of attributes. Incremental mechanisms to calculate the new knowledge granularity are first introduced. Then, the corresponding incremental algorithms are presented for attribute reduction based on the calculated knowledge granularity when multiple attributes are added to the decision system. Finally, experiments performed on UCI data sets and the complexity analysis show that the proposed incremental methods are effective and efficient to update attribute reducts with the increase of attributes. Investigation of matrix-based incremental mechanisms of knowledge granularity.Development of a matrix-based incremental updating attribution reduction method when adding multiple attributes.Presentation of an incremental reduction method based on non-matrix to increase the efficiency of the matrix-based method.

[1]  Chien-Chung Chan,et al.  A Rough Set Approach to Attribute Generalization in Data Mining , 1998, Inf. Sci..

[2]  Dun Liu,et al.  Incremental approaches for updating approximations in set-valued ordered information systems , 2013, Knowl. Based Syst..

[3]  Dun Liu,et al.  Incremental updating approximations in dominance-based rough sets approach under the variation of the attribute set , 2013, Knowl. Based Syst..

[4]  Maria E. Orlowska,et al.  Maintenance of Knowledge in Dynamic Information Systems , 1992, Intelligent Decision Support.

[5]  Yiyu Yao,et al.  Three-way decisions with probabilistic rough sets , 2010, Inf. Sci..

[6]  Wang Lei,et al.  A Matrix-Based Approach for Maintenance of Approximations under the Variation of Object Set , 2013 .

[7]  Yiyu Yao,et al.  Attribute reduction in decision-theoretic rough set models , 2008, Inf. Sci..

[8]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

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

[10]  Dun Liu,et al.  A fuzzy rough set approach for incremental feature selection on hybrid information systems , 2015, Fuzzy Sets Syst..

[11]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..

[12]  Da Ruan,et al.  Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems , 2012, Int. J. Approx. Reason..

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

[14]  Yun Zhu,et al.  Efficient parallel boolean matrix based algorithms for computing composite rough set approximations , 2016, Inf. Sci..

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

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

[17]  Potential Applications of Granular Computing in Knowledge Discovery and Data Mining , 1999 .

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

[19]  Hong Shen,et al.  Updating attribute reduction in incomplete decision systems with the variation of attribute set , 2014, Int. J. Approx. Reason..

[20]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[21]  Zhang Yi,et al.  Matrix approach to decision-theoretic rough sets for evolving data , 2016, Knowl. Based Syst..

[22]  Fan Shi-dong.  The Calculation of Knowledge Granulation and Its Application , 2002 .

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

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

[25]  Sun Lin.  Attribute Reduction Algorithm Based on Relative Granularity in Decision Tables , 2009 .

[26]  Wei-Zhi Wu,et al.  Approaches to knowledge reduction based on variable precision rough set model , 2004, Inf. Sci..

[27]  Yi Cheng,et al.  The incremental method for fast computing the rough fuzzy approximations , 2011, Data Knowl. Eng..

[28]  Jiye Liang,et al.  Attribute reduction: A dimension incremental strategy , 2013, Knowl. Based Syst..

[29]  Marzena Kryszkiewicz,et al.  FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute , 2008, Trans. Rough Sets.

[30]  Yi Pan,et al.  A Parallel Matrix-Based Method for Computing Approximations in Incomplete Information Systems , 2015, IEEE Transactions on Knowledge and Data Engineering.

[31]  Lin Sun,et al.  Feature selection using rough entropy-based uncertainty measures in incomplete decision systems , 2012, Knowl. Based Syst..

[32]  Jiye Liang,et al.  Ieee Transactions on Knowledge and Data Engineering 1 a Group Incremental Approach to Feature Selection Applying Rough Set Technique , 2022 .

[33]  Farokh B. Bastani.  Acknowledging TKDE's Fine Past and Looking to an Even Better Future , 1997, IEEE Trans. Knowl. Data Eng..

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

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

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

[37]  Geert Wets,et al.  A rough sets based characteristic relation approach for dynamic attribute generalization in data mining , 2007, Knowl. Based Syst..

[38]  Guoyin Wang,et al.  A Rough Set-Based Method for Updating Decision Rules on Attribute Values’ Coarsening and Refining , 2014, IEEE Transactions on Knowledge and Data Engineering.