Fang Min, Wang Bao-shu . Advance Ensemble Learning of Fuzzy Classification Rules Based on AdaBoost[J]. Journal of Electronics & Information Technology, 2005, 27(5): 835-837.
Citation:
Fang Min, Wang Bao-shu . Advance Ensemble Learning of Fuzzy Classification Rules Based on AdaBoost[J]. Journal of Electronics & Information Technology, 2005, 27(5): 835-837.
Fang Min, Wang Bao-shu . Advance Ensemble Learning of Fuzzy Classification Rules Based on AdaBoost[J]. Journal of Electronics & Information Technology, 2005, 27(5): 835-837.
Citation:
Fang Min, Wang Bao-shu . Advance Ensemble Learning of Fuzzy Classification Rules Based on AdaBoost[J]. Journal of Electronics & Information Technology, 2005, 27(5): 835-837.
A new learning algorithm of fuzzy classification rules is presented based on ensemble learning algorithm. By tuning the distribution of training instances during each AdaBoost iterative training, the classification rules with fuzzy antecedent and consequent are produced with genetic algorithm. The distribution of training instances participate in computing of the fitness function and the collaboration of rules which are complementary is taken into account during rules producing, so that the classification error rate is reduced and performance of the classification based on the fuzzy rules is improved.
Cordon O, del Jesus M J. Genetic learning of fuzzy classification systems cooperating with fuzzy reasoning methods [J].International Journal of Intelligent Systems.1998, 13(10/11):1025-3.0.CO;2-N' target='_blank'>[2]Merz C J. Using correspondence analysis to combine classifiers[J]. Machine Learning, 1999 36(1/2): 33 - 58.[3]Freund Y, Schapire R E. Experiments with a new boosting algorithm[C]. Proc. of the Thirteenth International Conference on Machine Learning, Morgan Kaufmann, 1996: 148- 156.[4]Gonzalez A, Herrera F. Multi-stage genetic fuzzy systems based on the iterative rule learning approach[J].Mathware Soft Computing.1997, 4:233-[5]Schapire R E. Theoretical views of boosting[C]. In Proc. 4th European Conference on Computational Learning Theory, 1999:1-10.[6]Freund Y. Boosting a weak learning algorithm by majority[J].Information and Computation, 1995, 2(121): 256 - 285.[7]Ishibuchi H, Nozaki K, Yamamoto N, et al.. Selecting fuzzy if-then rules for classification problems using genetic algorithms[J].IEEE Trans. on Fuzzy Systems.1995, 3(2):260-