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Knowledge Acquisition Tool for Learning Membership Function and Fuzzy Classification Rules from Numerical Data

by Fadl Mutaher Ba-alwi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 13
Year of Publication: 2013
Authors: Fadl Mutaher Ba-alwi
10.5120/10694-5603

Fadl Mutaher Ba-alwi . Knowledge Acquisition Tool for Learning Membership Function and Fuzzy Classification Rules from Numerical Data. International Journal of Computer Applications. 64, 13 ( February 2013), 24-30. DOI=10.5120/10694-5603

@article{ 10.5120/10694-5603,
author = { Fadl Mutaher Ba-alwi },
title = { Knowledge Acquisition Tool for Learning Membership Function and Fuzzy Classification Rules from Numerical Data },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 13 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 24-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number13/10694-5603/ },
doi = { 10.5120/10694-5603 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:17:03.883062+05:30
%A Fadl Mutaher Ba-alwi
%T Knowledge Acquisition Tool for Learning Membership Function and Fuzzy Classification Rules from Numerical Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 13
%P 24-30
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Generating suitable membership function (MF) is the core step of fuzzy classification system. This paper presents a novel learning algorithm that generates automatically reasonable MFs for quantitative attributes. In addition, a set of an appropriate fuzzy classification rules (FCRs) are discovered from a given numerical data. Each fuzzy rule (FR) is of the form IF-THEN rule. The antecedent IF-part and consequent THEN-part contain fuzzy sets. Since MFs are generated automatically, the proposed fuzzy learning algorithm can be viewed as a knowledge acquisition tool for classification problems. Experimental results on Iris dataset are presented to demonstrate the contribution of the proposed approach for generating MFs.

References
  1. Fernandez A. and Herrera F. , 2012 Linguistic Fuzzy Rules in Data Mining: Follow-Up Mamdani Fuzzy Modeling Principle. Springer-Verlag Berlin Heidelberg.
  2. Spada L. , 2009 Introduction to Fuzzy Sets and Fuzzy Logic, Reasonpark. Foligno, 17-19 Sep.
  3. Zadeh L. A. , 1996 Fuzzy Logic = Computing with Words, IEEE Transactions on Fuzzy Systems, vol. 4, No. 2, 103–111.
  4. Ishibuchi H. , 2000 Effect of Rule Weights in Fuzzy Rule-Based Classification Systems, IEEE Transactions on Fuzzy Systems, vol. 9, No. 4, 506-515.
  5. Bala, B. and Saroj, 2012 Discovering Fuzzy Censored Classification Rules: a Genetic Algorithm Approach, International Journal of Artificial Intelligence & Applications (IJAIA), vol. 3, No. 4, 175-188.
  6. Mansoori E. G. , Zolghadri M. J. , and Katebi S. D. , 2008 SGERD: A Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules from Data, IEEE Transactions on Fuzzy Systems, vol. 16, No. 4.
  7. Hu Y. , Chen R. S. , Tzeng G. H. , 2003 Finding Fuzzy Classification Rules using Data Mining Techniques, Pattern Recognition Letters 24, 509–519.
  8. Hu Y. , Chena R. , Tzengb G. , 2002 Mining Fuzzy Association Rules for Classification Problems, Computers & Industrial Engineering, vol. 43, 735–750.
  9. S´aez J. A. , Luengo J. , Herrera F. , 2011 Fuzzy Rule Based Classification Systems versus Crisp Robust Learners Trained in Presence of Class Noise's Effects: a Case of Study, IEEE, 11-th International Conference on Intelligent Systems Design and Applications, 1229-1234.
  10. Roubos J. A. , Setnes M. ,, Abonyi J. , 2003 Learning Fuzzy Classification Rules from Labeled Data, Information Sciences, 150, 77–93.
  11. Dehzangi O. , Zolghadri M. J. , Taheri S. , and Fakhrahmad S. M. , 2007 Efficient Fuzzy Rule Generation: A New Approach using Data Mining Principles and Rule Weighting. 4-th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)-IEEE.
  12. Rodr´?guez Y. , Falc´on R. , Varela A. , and Garc´?a M. M. , 2008 Learning Membership Functions for an Associative Fuzzy Neural Network, Springer-Verlag Berlin Heidelberg, 151-161.
  13. Wang L. , 1997 A Course in Fuzzy System and Control, International edition, Prentice-Hall International, Inc.
  14. Wang L. , and Mendel M. , 1992 Generating Fuzzy Rules by Learning from Examples, Transactions on Systems and Cybernetics, vol. 22, No. 6, 1414-1427.
  15. Hong T. , and Leeb C. , 1996 Induction of Fuzzy Rules and Membership Functions from Training Examples, Fuzzy Sets and Systems, vol. 84, 33-47.
  16. Chen S. M. , and Chen Y. C. , 2002 Automatically Constructing Membership Functions and Generating Fuzzy Rules using Genetic Algorithms. Cybernetics and Systems: An International Journal, vol. 33, No. 8, 841-863.
  17. Alcal´a-Fdez J. , Alcal R. , Gacto M. , and Herrera F. , 2009 Learning the Membership Function Contexts for Mining Fuzzy Association Rules by Using Genetic Algorithms,Fuzzy Set and Systems vol. 160, No. 7, 905-921.
  18. Huynh T. , Nguyen H. , Le B. , 2012 A Unified Design for the Membership Functions in Genetic Fuzzy Systems, International Journal of Computer Science Issues, vol. 9(3), No. 1, 1694-0814.
  19. Chen S. , and Fang Y. , 2005 A New Method to deal with Fuzzy Classification Problems by Tuning Membership Functions for Fuzzy Classification Systems, Journal of the Chinese Institute of Engineers, vol. 28, No. 1.
  20. Chen S. , and Tsai F. , 2005 A New Method to Construct Membership Functions and Generate Fuzzy Rules from Training Instances, Information and Management Sciences, vol. 16, No. 2, 47-72.
  21. Radha R. , and Rajagopalan S. P. , 2010 Generating Membership Values and Fuzzy Association Rules from Numerical Data, vol. 02, No. 08, 2705-2715.
  22. Cintra M. E. , Camargo H. A. , and Monard M. C. , 2008 A Study on Techniques for the Automatic Generation of Membership Functions for Pattern Recognition, Ibero-American Conference Publications, vol. 1, 1-10.
  23. Kumar S. , Bhalla P. , and Singh A. , 2009 Fuzzy Rule base Generation from Numerical Data using Biogeography-based Optimization, IE(I) Journal–ET, vol. 90, 1-6.
  24. Juang C. , 2005 Combination of Online Clustering and Q-value based GA for Reinforcement Fuzzy System Design. IEEE Transactions on Fuzzy Systems, vol. 13, No 3. 289-302.
  25. Ishibuchi H. , et al. 1993 Neural Networks that Learn from Fuzzy If Then Rules, IEEE Transaction on Fuzzy Systems, vol. 1, 85-97.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Classification Rule (FCR) knowledge acquisition tool Learning algorithm Membership function (MF)

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