Support vector machines ensemble with optimizing weights by genetic algorithm

Support Vector Machines (SVM) is a classification technique based on the structural risk minimization principle. It is characteristic of processing complex data and high accuracy. And the ensemble of classifiers often has better performance than any of component classifiers in the ensemble. In this paper, bagging, boosting, multiple SVM decision model (MSDM) and heterogeneous SVM decision model (HSDM) of SVM ensemble are compared on four data sets. For boosting and bagging, genetic algorithm is used to optimize the combining weights of component SVMs. Experiment results show that SVM ensemble with optimizing weights by genetic algorithm could gain higher accuracy.