In classification issues, support vector machine (SVM) has an excellent ability to solve the problems. However, the single classifier often gets in the local solution, when the classifier can’t build by a suitable training set or has the poor statistical estimation in training process. Multiple classification system is proposed to overcome these problems from the single classifier. The advantage of multiple classification system is it can gain the more effective classification information from each single classifier, and improve the classification performance via this information. The final step of multiple classification system is the combination or fusion of multiple classifiers, and chooses a proper method to combine or fuse the multiple classification system to make the final decision is the most important task. In this thesis, we try to build a multiple classification system via SVM classifiers, and assume that there are correlations between these classifiers, and applying the Choquet fuzzy integral fusion algorithm with respect to L-measure with a more sensitive fuzzy density we proposed to decrease the influences of the interaction between the classifiers. Experiment results show the Choquet fuzzy integral algorithm with respect to L-measure with the fuzzy density we proposed obtains the advancement in terms of the performance of classification.
