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Optimization of Decision Rules in Fuzzy Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 51 - Number 3 |
| Year of Publication: 2012 |
| Authors: Renuka Arora, Sudesh Kumar |
10.5120/8021-0505
|
Renuka Arora, Sudesh Kumar . Optimization of Decision Rules in Fuzzy Classification. International Journal of Computer Applications. 51, 3 ( August 2012), 13-17. DOI=10.5120/8021-0505
Abstract
There are various advances in data collection that can intelligently and automatically analyze and mine knowledge from large amounts of data. World Wide Web as a global information system has flooded us with a tremendous amount of data and information Discovery of knowledge and decision-making directly from such huge volumes of data contents is a real challenge. The Knowledge Discovery in Databases (KDD) is the process of extracting the knowledge from huge data collection. Data mining is a step of KDD in which patterns or models are extracted from data by using some automated techniques. Discovering knowledge in the form of classification rules is one of the most important tasks of data mining. Discovery of comprehensible, concise and effective rules helps us to make right decisions. Therefore, several Machine Learning techniques are applied for discovery of classification rules. Recently there have been several applications of genetic algorithms for effective rules with high predictive accuracy.
References
- A. Freitas, "Data Mining and Knowledge Discovery with Evolutionary Algorithm", Natural Computing Series, Springer-Verlag, New York, USA, 2002.
- A. Freitas, "A survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery", Advances in Evolutionary Computation Theory and Applications, Springer-Verlag, New York, USA, pp. 819-845, 2003.
- Kosko, "Neural Networks and Fuzzy Systems", Prentice-Hall, Englewood Cliffs, NJ, 1992.
- Liu, W. Hsu and S. Chen, "Using General Impressions to Analyze Discovered Classification Rules", In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, CA, USA, AAAI Press, Portland Oregon, USA, pp. 31-36, 1997.
- Mota, H. Ferreira and A. Rosa, "Independent and simultaneous evolution of fuzzy sleep classifiers by genetic algorithms", Proc. Genetic and Evolutionary Computation Conf. (GECCO-99), Morgan Kaufmann, pp. 1622-1629, 1999.
- T. Lin and C. S. G. Lee, "Neural-Network-based fuzzy logic control and decision system", IEEE Trans. Comput. , pp. 1320-1336, 1991.
- E. Goldberg, "Genetic Algorithms in Search, Optimization and Machine Learning", Addison-Wesley Publishing Company, Inc. MA, New York, 1989.
- Walter and C. K. Mohan, "ClaDia: A fuzzy classifier system for disease diagnosis", In Proc. Congress on Evolutionary Computation (CEC-2000), La Jolla, CA, USA, vol. 2, 2000.
- Noda, Alex A. Freitas and H. S. Lopes, "Discovering Interesting Prediction Rules with a Genetic Algorithm", In Proc. Congress on Evolutionary Computation (CEC-99), Washington D. C. , USA, pp. 1322-1329, July 1999.
- Eghbal G. Mansoori, Mansoor J. Zolghadri and Seraj D. Katebi, "SGERD: A Steady-State Genetic Algorithm For Extracting Fuzzy Classification Rules From Data," IEEE Trans. Fuzzy Syst. , vol. 16, no. 4, pp. 1061-1071, Aug. 2008.
- Rothlauf, Representations for Genetic and Evolutionary Algorithms, Physica-Verlag, Heidelberg, Germany, 2002.
- J. Klir and B. Yuan, "Fuzzy Sets and Fuzzy Logic: Theory and Applications", Prentice-Hall, 1995.
- Ishibuchi, T. Nakashima and T. Murata, "Performance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems", IEEE Trans. Syst. ,Man, Cybern. , Part B, vol. 29, pp. 601-618, 1999.
- Ishibuchi, T. Nakashima and T. Kuroda, "A Hybrid Fuzzy GBML Algorithm for Designing Compact Fuzzy Rule-Based Classification Systems", In Proceedings of the 9th IEEE Int. Conf. Fuzzy Systems (FUZZ IEEE-2000), San Antonio, TX, USA, pp. 706-711, 2000.
- H. M. Chen and S. Y. Ho, "Designing an Optimal Evolutionary Fuzzy Decision Tree for Data Mining", In Proc. of the Genetic and Evolutionary Computation Conference (GECCO-2001), San Francisco, California, USA, Morgan Kaufmann, San Francisco, California, USA, pp. 943-950, 2001b.
- D. Falco, A. D. Cioppa, A. Iazzetta and E. Tarantion, "An Evolutionary Approach for Automatically Extracting Intelligible Classification Rules", Knowledge and Information Systems, Springer-Verlag, New York, USA, vol. 7, no. 2, pp. 179-201, 2005.
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