A Rough Set-Based Method for Updating Decision Rules on Attribute Values’ Coarsening and Refining

Rule induction method based on rough set theory (RST) has received much attention recently since it may generate a minimal set of rules from the decision system for real-life applications by using of attribute reduction and approximations. The decision system may vary with time, e.g., the variation of objects, attributes and attribute values. The reduction and approximations of the decision system may alter on Attribute Values' Coarsening and Refining (AVCR), a kind of variation of attribute values, which results in the alteration of decision rules simultaneously. This paper aims for dynamic maintenance of decision rules w.r.t. AVCR. The definition of minimal discernibility attribute set is proposed firstly, which aims to improve the efficiency of attribute reduction in RST. Then, principles of updating decision rules in case of AVCR are discussed. Furthermore, the rough set-based methods for updating decision rules in the inconsistent decision system are proposed. The complexity analysis and extensive experiments on UCI data sets have verified the effectiveness and efficiency of the proposed methods.

[1]  Da Ruan,et al.  Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining , 2012, Knowl. Based Syst..

[2]  Ertem Tuncel,et al.  Incremental Maintenance of Online Summaries Over Multiple Streams , 2008, IEEE Transactions on Knowledge and Data Engineering.

[3]  Xiaohua Hu,et al.  Rule Discovery from Databases with Decision Matrices , 1996, ISMIS.

[4]  Sen Guo,et al.  A novel dynamic incremental rules extraction algorithm based on rough set theory , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[5]  K. Thangavel,et al.  Dimensionality reduction based on rough set theory: A review , 2009, Appl. Soft Comput..

[6]  Donato Malerba,et al.  The effects of pruning methods on the predictive accuracy of induced decision trees , 1999 .

[7]  Guoyin Wang,et al.  RRIA: A Rough Set and Rule Tree Based Incremental Knowledge Acquisition Algorithm , 2003, Fundam. Informaticae.

[8]  Sushmita Mitra,et al.  Evolutionary Rough Feature Selection in Gene Expression Data , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[10]  Chun-Che Huang,et al.  Alternative rule induction methods based on incremental object using rough set theory , 2013, Appl. Soft Comput..

[11]  Jiye Liang,et al.  Attribute reduction for dynamic data sets , 2013, Appl. Soft Comput..

[12]  Yen-Liang Chen,et al.  Constructing a decision tree from data with hierarchical class labels , 2009, Expert Syst. Appl..

[13]  Jianhui Lin,et al.  A Rough-Set-Based Incremental Approach for Updating Approximations under Dynamic Maintenance Environments , 2013, IEEE Transactions on Knowledge and Data Engineering.

[14]  Chun-Che Huang,et al.  Rule induction based on an incremental rough set , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[15]  Yitian Xu,et al.  A dynamic attribute reduction algorithm based on 0-1 integer programming , 2011, Knowl. Based Syst..

[16]  Huaxiong Li,et al.  Risk Decision Making Based on Decision-theoretic Rough Set: A Three-way View Decision Model , 2011 .

[17]  Wojciech Ziarko,et al.  DATA‐BASED ACQUISITION AND INCREMENTAL MODIFICATION OF CLASSIFICATION RULES , 1995, Comput. Intell..

[18]  Xiaohua Hu,et al.  Data Mining Using Attribute-Oriented Generalization and Information Reduction , 1997 .

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

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

[21]  Sungho Jo,et al.  Incremental Online Learning of Robot Behaviors From Selected Multiple Kinesthetic Teaching Trials , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

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

[23]  Dongyi Ye,et al.  A novel and better fitness evaluation for rough set based minimum attribute reduction problem , 2013, Inf. Sci..

[24]  Zhongzhi Shi,et al.  Extended rough set-based attribute reduction in inconsistent incomplete decision systems , 2012, Inf. Sci..

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

[26]  B. K. Tripathy,et al.  Enhanced Rule Induction Using Incremental Approach fora Dynamic Information System , 2011 .

[27]  Da Ruan,et al.  An Incremental Approach for Inducing Knowledge from Dynamic Information Systems , 2009, Fundam. Informaticae.

[28]  Gerald Schaefer,et al.  Rough Sets and near Sets in Medical Imaging: a Review , 2022 .

[29]  Min Chen,et al.  Rough Cluster Quality Index Based on Decision Theory , 2009, IEEE Transactions on Knowledge and Data Engineering.

[30]  Roman Slowinski,et al.  Incremental Induction of Decision Rules from Dominance-based Rough Approximations , 2003, RSKD.

[31]  Sung-Suk Kim,et al.  Incremental modeling with rough and fine tuning method , 2011, Appl. Soft Comput..

[32]  Yumin Chen,et al.  A rough set approach to feature selection based on power set tree , 2011, Knowl. Based Syst..

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

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

[35]  Francisco Herrera,et al.  A model of consensus in group decision making under linguistic assessments , 1996, Fuzzy Sets Syst..

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

[37]  Mohamed-Slim Alouini,et al.  Outage Analysis for Underlay Cognitive Networks Using Incremental Regenerative Relaying , 2013, IEEE Transactions on Vehicular Technology.

[38]  Qiang Shen,et al.  A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction , 2010, IEEE Transactions on Knowledge and Data Engineering.

[39]  Tsau Young Lin,et al.  Context-Free Fuzzy Sets in Data Mining Context , 1999, RSFDGrC.

[40]  Zhenmin Tang,et al.  Minimum cost attribute reduction in decision-theoretic rough set models , 2013, Inf. Sci..

[41]  Liang Liu,et al.  Attribute selection based on a new conditional entropy for incomplete decision systems , 2013, Knowl. Based Syst..

[42]  Masahiro Inuiguchi,et al.  Variable-precision dominance-based rough set approach and attribute reduction , 2009, Int. J. Approx. Reason..

[43]  Shusaku Tsumoto.  A new framework for incremental rule induction based on rough sets , 2011, 2011 IEEE International Conference on Granular Computing.

[44]  Shaojie Qiao,et al.  A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values , 2010, Int. J. Intell. Syst..

[45]  Witold Pedrycz,et al.  Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications , 2010, Int. J. Approx. Reason..

[46]  Da Ruan,et al.  Neighborhood rough sets for dynamic data mining , 2012, Int. J. Intell. Syst..

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

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

[49]  Lei Zhang,et al.  Sample Pair Selection for Attribute Reduction with Rough Set , 2012, IEEE Transactions on Knowledge and Data Engineering.

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

[51]  Qingguo Li,et al.  Reduction about approximation spaces of covering generalized rough sets , 2010, Int. J. Approx. Reason..

[52]  Yiyu Yao,et al.  Interpreting Low and High Order Rules: A Granular Computing Approach , 2007, RSEISP.

[53]  Piotr Bilski,et al.  Rough-Sets-Based Reduction for Analog Systems Diagnostics , 2011, IEEE Transactions on Instrumentation and Measurement.

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

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

[56]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Hierarchical multi-label classification using local neural networks , 2014, J. Comput. Syst. Sci..

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