Dependence-space-based attribute reduction in consistent decision tables

This paper proposes a novel approach to attribute reduction in consistent decision tables within the framework of dependence spaces. For a consistent decision table $$(U,A\cup \{d\}),$$ an equivalence relation r on the conditional attribute set A and a congruence relation R on the power set of A are constructed, respectively. Two closure operators, Tr and TR, and two families of closed sets, $${\mathcal C}_r$$ and $${\mathcal C}_R,$$ are then constructed with respect to the two equivalence relations. After discussing the properties of $${\mathcal C}_r$$ and $${\mathcal C}_R,$$ the necessary and sufficient condition for $${\mathcal C}_r={\mathcal C}_R$$ is obtained and employed to formulate an approach to attribute reduction in consistent decision tables. It is also proved, under the condition $${\mathcal C}_r={\mathcal C}_R,$$ that a relative reduct is equivalent to a $$R$$-reduction defined by Novotny and Pawlak (Fundam Inform 16:275–287, 1992).

[1]  Engelbert Mephu Nguifo,et al.  Introduction concept lattice-based theory, methods and tools for knowledge discovery in databases: Applications , 2003, Appl. Artif. Intell..

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

[3]  S. Pal,et al.  Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis , 2007, IEEE Transactions on Knowledge and Data Engineering.

[4]  Manfred M. Fischer,et al.  A Rough Set Approach for the Discovery of Classification Rules in Interval-Valued Information Systems , 2008, Int. J. Approx. Reason..

[5]  U. M. Feyyad Data mining and knowledge discovery: making sense out of data , 1996 .

[6]  Geoff Holmes,et al.  Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..

[7]  T. Medhat,et al.  Topological reduction of information systems , 2005 .

[8]  Wen-Xiu Zhang,et al.  Knowledge reduction based on the equivalence relations defined on attribute set and its power set , 2007, Inf. Sci..

[9]  S. Greco,et al.  Rough set based processing of inconsistent information in decision analysis , 2000 .

[10]  Wei-Zhi Wu,et al.  Knowledge reduction in random information systems via Dempster-Shafer theory of evidence , 2005, Inf. Sci..

[11]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[12]  Andrzej Skowron,et al.  A rough set approach to knowledge discovery , 2002, International Journal of Intelligent Systems.

[13]  Yee Leung,et al.  Knowledge acquisition in incomplete information systems: A rough set approach , 2006, Eur. J. Oper. Res..

[14]  Yiyu Yao,et al.  A Comparative Study of Formal Concept Analysis and Rough Set Theory in Data Analysis , 2004, Rough Sets and Current Trends in Computing.

[15]  Yiyu Yao,et al.  Concept lattices in rough set theory , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[16]  Simon C. K. Shiu,et al.  Combining feature reduction and case selection in building CBR classifiers , 2006, IEEE Transactions on Knowledge and Data Engineering.

[17]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[18]  Fei-Yue Wang,et al.  Reduction and axiomization of covering generalized rough sets , 2003, Inf. Sci..

[19]  Kenji Satou,et al.  Extraction of knowledge on protein-protein interaction by association rule discovery , 2002, Bioinform..

[20]  David A. Bell,et al.  Knowledge Discovery from Decision Tables by the Use of Multiple-Valued Logic , 2004, Artificial Intelligence Review.

[21]  Siddhartha Bhattacharyya,et al.  Knowledge-intensive genetic discovery in foreign exchange markets , 2002, IEEE Trans. Evol. Comput..

[22]  Xia Wang,et al.  Dependence space of concept lattices based on rough set , 2006, 2006 IEEE International Conference on Granular Computing.

[23]  S. Tsumoto,et al.  Rough set methods and applications: new developments in knowledge discovery in information systems , 2000 .

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

[25]  Qiang Shen,et al.  Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches , 2004, IEEE Transactions on Knowledge and Data Engineering.

[26]  Paulo Martins Engel,et al.  Using Knowledge Discovery to Identify Analysis Patterns for Geographic Database , 2003, ICEIS.

[27]  Andrzej Skowron,et al.  Boolean Reasoning for Decision Rules Generation , 1993, ISMIS.

[28]  Yee Leung,et al.  Maximal consistent block technique for rule acquisition in incomplete information systems , 2003, Inf. Sci..

[29]  Malcolm J. Beynon,et al.  Reducts within the variable precision rough sets model: A further investigation , 2001, Eur. J. Oper. Res..

[30]  Tong Heng Lee,et al.  Evolutionary computing for knowledge discovery in medical diagnosis , 2003, Artif. Intell. Medicine.

[31]  Wei-Zhi Wu,et al.  Attribute reduction based on evidence theory in incomplete decision systems , 2008, Inf. Sci..

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

[33]  Ramin Yasdi,et al.  Combining Rough Sets Learning- and Neural Learning-method to deal with uncertain and imprecise information , 1995, Neurocomputing.

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

[35]  Zdzislaw Pawlak,et al.  On a problem concerning dependence spaces , 1992, Fundam. Informaticae.