تعداد نشریات | 27 |
تعداد شمارهها | 566 |
تعداد مقالات | 5,822 |
تعداد مشاهده مقاله | 8,158,941 |
تعداد دریافت فایل اصل مقاله | 5,458,450 |
NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS | ||
Iranian Journal of Fuzzy Systems | ||
مقاله 7، دوره 3، شماره 1، تیر 2006، صفحه 77-89 اصل مقاله (260.05 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22111/ijfs.2006.439 | ||
نویسندگان | ||
MEHDI EFTEKHARI ![]() | ||
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN | ||
چکیده | ||
Designing an effective criterion for selecting the best rule is a major problem in the process of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidence and support or combined measures of these are used as criteria for fuzzy rule evaluation. In this paper new entities namely precision and recall from the field of Information Retrieval (IR) systems is adapted as alternative criteria for fuzzy rule evaluation. Several different combinations of precision and recall are redesigned to produce a metric measure. These newly introduced criteria are utilized as a rule selection mechanism in the method of Iterative Rule Learning (IRL) of FLC. In several experiments, three standard datasets are used to compare and contrast the novel IR based criteria with other previously developed measures. Experimental results illustrate the effectiveness of the proposed techniques in terms of classification performance and computational efficiency. | ||
کلیدواژهها | ||
Fuzzy classification؛ Rule evaluation criteria؛ Information retrieval؛ Iterative rule learning | ||
مراجع | ||
[1] R. Agrawal, H. Mannila, R. Srikant, H. Toivonen and A. I. Verkamo, Fast discovery of association rules, In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Editors.) Advances in Knowledge Discovery and Data Mining, AAAI Press, (1996), 307-328. [2] R. Agrawal, and R. Srikant, Fast algorithms for mining association rules, Proc. of 20th International Conference on Very Large Data Bases, (1994), 487-499. [3] Richardo Baeza-Yates and Berthier Ribeiro-Neto, Modern information retrieval, New York, ACM Press, Addison-Wesley, 1999. [4] L. B. Booker, D. E. Goldberg and J. H. Holland, Classifier systems and genetic algorithms, Artificial Intelligence, 40 (1989), 235-282. [5] L. Castillo, A. Gonzanlez and R. Perez, Including a simplicity criterion in the selection of the best rule in a genetic fuzzy learning algorithm, Fuzzy Sets and Systems, 120 (2) (2001), 309-321. [6] L. Castro, J. J. Castro-Schez and J. M. Zurita, Use of a fuzzy machine learning technique in the knowledge acquisition process, Fuzzy Sets and Systems, 123 (3) ( 2001), 307-320. [7] S. M. Chen and C. H. Yu, A new method to generate fuzzy rules from training instances for handling classification problems, Cybernetics and Systems, 34 (2003), 217-232. [8] O. Cordon, F. Herrera, F. Hoffman and L. Magdalena, Genetic Fuzzy Systems, World Scientific, 2001. [9] K. A. De Jong, W. M. Spears and F. D. Gordon, Using genetic algorithm for concept learning, Machine Learning, 13 (1993), 161-188. [10] P. A. Devijver and J. Kittler, Pattern Recognition: A statistical Approach, Englewood Cliffs: Prentice Hall, 1982. [11] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996. [12] A. Gonzalez and R. Perez, Completeness and consistency conditions for learning fuzzy rules, Fuzzy Sets and Systems, 96 (1998), 37–51. [13] A. Gonzanlez and R. Perez, SLAVE: A genetic learning system based on an iterative approach, IEEE Trans. On Fuzzy Systems, 7 (2) (1999), 176-191. [14] F. Herrera, M. Lozano and J. L. Verdegay, Generating rules from examples using genetic algorithms. Fuzzy Logic and Soft Computing, Word Scientific, (1995), 11-20. [15] J. H. Holland and Escaping Britleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems, Machine Learning: An AI Approach, Vol. 2, Morgan-Kaufmann, (1986), 593-623. [16] H. Ishibuchi and T. Yamaoto, Comparison of heuristic criteria for fuzzy rule selection in classification problems, Fuzzy Optimization and Decision Making, 3 (2) (2004), 119-139. [17] H. Ishibuchi, T. Yamamoto and T. Nakashima, Fuzzy data mining: Effect of fuzzy discretization, Proc. of 1st IEEE International Conference on Data Mining, (2001), 241-248. [18] C. Z. Janikow, A knowledge intensive genetic algorithm for supervised learning, Machine Learning , 13 (1993), 198-228. [19] D. E. Kraft and A. Bookstein, Evaluation of information retrieval system: A decision theory approach, Journal of the American Society for Information Science, 29 (1978), 31-40. [20] D. J. Newman and S. Hettich, C.L. Blake and C.J. Merz, (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html], Irvine, CA: University of California, Department of Information and Computer Science. [21] J. A. Roubos and M. Setnes, Compact fuzzy models through complexity reduction and evolutionary optimization. In FUZZ-IEEE, San Antonio, USA, (2000), 762-767. [22] J. A. Roubos, M. Setnes and J. Abonyi, Learning fuzzy classification rules from data, Developments in Soft Computing, In John, R. And Birkenhead, R. (Editors), Springer - Verlag Berlin/Heidelberg, (2001), 108-115. [23] M. Setnes, R. Babuska, U. Kaymak and H. R. van Nauta Lemke, Similarity measures in fuzzy rule base simplification, IEEE Trans. SMC-B, 28 (1998), 376-386. [24] S. F. Smith, A learning system based on genetic adaptive algorithms, PhD Thesis, University of Pittsburgh, 1980. [25] C. J. Van Rijsbergen, Information Retrieval, Butterworths, 1979. [26] G. Venturini, SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts. European Conference on Machine Learning, (1993), 280-296. [27] L. X. Wang and J. M. Mendel, Generating fuzzy rules by learning from examples, IEEE Trans. Syst., Man, Cybern., 22 (6) (1992), 1414-1427. | ||
آمار تعداد مشاهده مقاله: 2,415 تعداد دریافت فایل اصل مقاله: 1,210 |