Extended Pawlak's Flow Graphs and Information Theory

Flow graph is an effective and graphical tool of knowledge representation and analysis. It explores dependent relation between knowledge in the form of information flow quantity. However, the quantity of flow can not exactly represent the functional dependency between knowledge. In this paper, we firstly present an extended flow graph using concrete information flow, and then give its interpretation under the framework of information theory. Subsequently, an extended flow graph generation algorithm based on the significance of attribute is proposed in virtue of mutual information. In addition, for the purpose of avoiding over-fitting and reducing store space, a reduction method about this extension using information metric has also been developed.

[1]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[2]  Zdzislaw Pawlak,et al.  Probability, Truth and Flow Graph , 2003, RSKD.

[3]  D. R. Fulkerson,et al.  Flows in Networks. , 1964 .

[4]  Zdzislaw Pawlak,et al.  Some remarks on conflict analysis , 2005, Eur. J. Oper. Res..

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

[6]  Andrzej Skowron,et al.  Reasoning Based on Information Changes in Information Maps , 2003, RSFDGrC.

[7]  Wen Yan,et al.  The Computational Complexity of Inference Using Rough Set Flow Graphs , 2005, RSFDGrC.

[8]  Feng-Hsu Wang.  On acquiring classification knowledge from noisy data based on rough set , 2005, Expert Syst. Appl..

[9]  Zdzislaw Pawlak,et al.  Decision Trees and Flow Graphs , 2006, RSCTC.

[10]  Zdzislaw Pawlak,et al.  Rough Sets and Flow Graphs , 2005, RSFDGrC.

[11]  Andrzej Skowron,et al.  Rough-Fuzzy Hybridization: A New Trend in Decision Making , 1999 .

[12]  Andrzej Skowron,et al.  Transactions on Rough Sets VIII , 2008, Trans. Rough Sets.

[13]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[14]  Zdzislaw Pawlak,et al.  Flow Graphs and Decision Algorithms , 2003, RSFDGrC.

[15]  S. Tsumoto,et al.  On Learning Decision Rules From Flow Graphs , 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

[16]  Nick Cercone,et al.  Rule learning: Ordinal prediction based on rough sets and soft-computing , 2006, Appl. Math. Lett..

[17]  Alicja Mieszkowicz-Rolka,et al.  Flow Graphs and Decision Tables with Fuzzy Attributes , 2006, ICAISC.

[18]  Ryszard Tadeusiewicz,et al.  Artificial Intelligence and Soft Computing - ICAISC 2006, 8th International Conference, Zakopane, Poland, June 25-29, 2006, Proceedings , 2006, International Conference on Artificial Intelligence and Soft Computing.

[19]  Peter Szolovits,et al.  What Is a Knowledge Representation? , 1993, AI Mag..

[20]  Andrzej Czyzewski,et al.  Processing of Musical Metadata Employing Pawlak's Flow Graphs , 2004, Trans. Rough Sets.

[21]  Zdzislaw Pawlak,et al.  Some Issues on Rough Sets , 2004, Trans. Rough Sets.

[22]  Wen Yan,et al.  An Efficient Algorithm for Inference in Rough Set Flow Graphs , 2006, Trans. Rough Sets.

[23]  Jigui Sun,et al.  Interpretation of Extended Pawlak Flow Graphs Using Granular Computing , 2008, Trans. Rough Sets.

[24]  Yiyu Yao,et al.  Perspectives of granular computing , 2005, 2005 IEEE International Conference on Granular Computing.

[25]  Puntip Pattaraintakorn,et al.  Knowledge discovery by rough sets mathematical flow graphs and its extension , 2008 .

[26]  Yingxu Wang,et al.  The Theoretical Framework of Cognitive Informatics , 2007, Int. J. Cogn. Informatics Nat. Intell..

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

[28]  Jerzy W. Grzymala-Busse,et al.  Transactions on Rough Sets I , 2004, Lecture Notes in Computer Science.

[29]  Yingxu Wang.  On Cognitive Informatics , 2003 .

[30]  Frances E. Allen,et al.  Control-flow analysis , 2022 .

[31]  Zdzislaw Pawlak,et al.  Rough sets, decision algorithms and Bayes' theorem , 2002, Eur. J. Oper. Res..

[32]  Gerhard Lakemeyer,et al.  The logic of knowledge bases , 2000 .

[33]  Zdzislaw Pawlak,et al.  Decisions rules and flow networks , 2004, Eur. J. Oper. Res..

[34]  Andrzej Czyzewski,et al.  Musical Metadata Retrieval with Flow Graphs , 2004, Rough Sets and Current Trends in Computing.

[35]  Krzysztof Pancerz,et al.  Flow Graphs as a Tool for Mining Prediction Rules of Changes of Components in Temporal Information Systems , 2007, RSKT.

[36]  Zdzislaw Pawlak,et al.  A Treatise on Rough Sets , 2005, Trans. Rough Sets.

[37]  Zdzislaw Pawlak,et al.  Flow Graphs and Data Mining , 2005, Trans. Rough Sets.

[38]  Jigui Sun,et al.  An Extension of Pawlak's Flow Graphs , 2006, RSKT.

[39]  Zdzisław Pawlak,et al.  The Rough Set View on Bayes' Theorem , 2002, AFSS.

[40]  Sadaaki Miyamoto,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.

[41]  Zdzisław Pawlak,et al.  Decision Algorithms, Bayes’ Theorem and Flow Graphs , 2003 .

[42]  Michio Sugeno,et al.  Advances in Soft Computing — AFSS 2002 , 2002, Lecture Notes in Computer Science.

[43]  Tsau Young Lin,et al.  Heuristically Fast Finding of the Shortest Reducts , 2004, Rough Sets and Current Trends in Computing.

[44]  Andrzej Skowron,et al.  Transactions on Rough Sets III , 2005, Trans. Rough Sets.

[45]  Cory J. Butz.  Current Trends in Rough Set Flow Graphs , 2008 .