Abstract
The typical fuzzy classifier consists of rules each one describing one of the classes. This paper presents a new fuzzy classifier with probabilistic IF-THEN rules. A learning algorithm based on the gradient descent method is proposed to identify the probabilistic IF-THEN rules from the training data set. This new fuzzy classifier is finally applied to the well-known Wisconsin breast cancer classification problem, and a compact, interpretable and accurate probabilistic IF-THEN rule base is achieved.
Keywords
- Membership Function
- Fuzzy Rule
- Fuzzy Model
- Fuzzy Cluster
- Fuzzy Entropy
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Wang, J.-S., Lee, C.S.G.: Self-adaptive neuro-fuzzy inference systems for classification applications. IEEE Transactions on Fuzzy Systems 10(6), 790–802 (2002)
Setnes, M., Roubos, H.: Ga-fuzzy modeling and classification: Complexity and performance. IEEE Transactions on Fuzzy Systems 8(5), 509–522 (2000)
Peña Reyes, C.A., Sipper, M.: A fuzzy-genetic approach to breast cancer diagnosis. Artificial Intelligence in Medicine 17, 131–155 (1999)
Setnes, M., Babuška, R.: Fuzzy relational classifier trained by fuzzy clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 29(5), 619–625 (1999)
Gath, I., Geva, A.B.: Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 773–781 (1989)
Abonyi, J., Szeifert, F.: Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognition Letters 24, 2195–2207 (2003)
Quinlan, J.R.: Improved use of continuous attributes in c4.5. J. Artificial Intell. Res. 4, 77–90 (1996)
Janos Abonyi, J.A., Szeifert, F.: Data-driven generation of compact, accurate,and linguistically sound fuzzy classifiers based on a decision-tree initialization. International Journal of Approximate Reasoning 32, 1–21 (2003)
Bojarczuk, C.C., Lopes, H.S., Freitas, A.A., Michalkiewicz, E.L.: A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets. Artificial Intelligence in Medicine 30, 27–48 (2004)
Nauck, D., Kruse, R.: Obtaining interpretable fuzzy classification rules from medical data. Artificial Intell. Med. 16, 149–169 (1999)
Ravi, V., Zimmermann, H.-J.: Fuzzy rule based classification with featureselector and modified threshold accepting. European Journal of Operational Research 123, 16–28 (2000)
Lee, H.-M., Chen, C.-M., Chen, J.-M., Jou, Y.-L.: An efficient fuzzy classifier with feature selection based on fuzzy entropy. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 31(3), 426–432 (2001)
Setiono, R.: Generating concise and accurate classification rules for breast cancer diagnosis. Artificial Intelligence in Medicine 18, 205–219 (2000)
Meesad, P., Yen, G.G.: Combined numerical and linguistic knowledge representation and its application to medical diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 33(2), 206–222 (2003)
Tang, Y., Sun, S.: A Mixture Model of Classical Fuzzy Classifiers. In: Li, Q., Wang, G., Feng, L. (eds.) WAIM 2004. LNCS, vol. 3129, pp. 616–621. Springer, Heidelberg (2004)
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Lv, H., Zhu, B., Tang, Y. (2007). Fuzzy Classifier with Probabilistic IF-THEN Rules. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds) Foundations of Fuzzy Logic and Soft Computing. IFSA 2007. Lecture Notes in Computer Science(), vol 4529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72950-1_66
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DOI: https://doi.org/10.1007/978-3-540-72950-1_66
Publisher Name: Springer, Berlin, Heidelberg
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