An improved inductive learning algorithm with a preanalysis of data
We propose an improved inductive learning procedure, IPI, to derive classification rules from examples. A preanalysis of data is included which assigns higher weights to those values of the attributes which occur more often in the positive than in the negative examples. The inductive learning problem is represented as a covering problem in integer programming which is solved by a modified greedy algorithm. The results are very encouraging, and are shown for a well-known M3 (Monk's) problem.
Keywordsinductive learning learning from examples covering problem 0–1 programming greedy algorithm
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- Garfinkel R. S. and Nemhauser G. L. (1978) Integer programming. John Wiley & Sons, New York-London-Sydney-Toronto.Google Scholar
- Iwański C. and Szkatula G (1991) Inductive learning supported by integer programming. Computers and Artificial Intelligence 10, pp. 57–66.Google Scholar
- Kacprzyk J. and Iwański C. (1991) Inductive learning from incomplete and imprecise examples. In: B. Bouchon-Meunier, R.R. Yager and L.A. Zadeh (Eds.), Uncertainty in Knowledge Bases Springer-Verlag, Berlin, pp. 424–430.Google Scholar
- Kacprzyk J. and Iwański C. (1992) Fuzzy logic with linguistic quantifiers in inductive learning, In: L.A. Zadeh and J. Kacprzyk (Eds.), Fuzzy Logic for the Management of Uncertainty, Wiley, New York, pp. 465–478.Google Scholar
- Kacprzyk J. and Szkatula G. (1994) Machine learning from examples under errors in data, Proceedings of Fifth International Conference in Information Processing and Management of Uncertainty in Knowledge-Based Systems-IPMU'94 (Paris, France), Vol. 2, pp. 1047–1051.Google Scholar
- Kacprzyk J. and Szkatula G. (1996) An algorithm for learning from erroneous and incorrigible examples, International Journal of Intelligent Systems 11, 565–582.Google Scholar
- Michalski R.S. (1973) Discovering classification rules using variable-valued logic system VL1. Proceedings of the Third International Joint Conference on Artificial Intelligence (IJCAI), p. 162–172.Google Scholar
- Michalski R.S. (1983) A theory and methodology of inductive learning. In: R. Michalski, J. Carbonell and T.M. Mitchell (Eds.), Machine Learning. Tioga Press, Palo Alto, CA.Google Scholar
- Michalski R.S., I. Mozetic, J. Hong and N. Lovrac (1986) The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. Proceedings of the Fifth National Conference on Artificial Intelligence (Philadelphia, PA). Morgan Kaufmann, San Mateo, CA, pp. 1041–1045.Google Scholar
- Thrun S., Bala J., Bloedorn E., Bratko I., Cestnik B., Cheng J., De Jong K., Dzeroski S., Fahlman S.E., Fisher D,, Hamann R., Kaufman K., Keller S., Kononenko I., Kreuziger J., Michalski R.S., Mitchell T., Pachowicz P., Reich Y., Vafaie H., Van de Welde W., Wenzel W., Wnek J., Zhang J. (1991) The MONK'S Problems. A Performance Comparison of Different Learning Algorithms. Carnegie Mellon University, Rep. CMU-CS-91–197.Google Scholar