Logistic regression algorithm and SVM algorithm are two well-known classification algorithms but when the multi-collinearity between independent variables occurs in above two algorithms, their classifying performance will always be not good. An improved classification algorithm combining the Choquet integral with respect to the lambda-measure based on gamma-support is proposed by our previous work. In this paper, we replaced the more sensitive fuzzy measure, L-measure with the lambda-measure in above improved classification algorithm, and we obtained a further improved algorithm, called Choquet integral logistic regression algorithm based on L-measure and gamma-support. For evaluating the performances of the SVM, logistic regression and the Choquet integral logistic regression algorithm with gamma-support based on P-measure, lambda-measure and L-measure, respectively, a real data experiment by using leave-one-out cross-validation accuracy is conducted. Experimental result shows that our new algorithm has the best performance.
Relation:
Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2 :771-776
