Bisecting grid-based SVM ensemble

According to the fact that the bootstrap in SVM ensemble learning can´t generate the committee classifiers with big differences,SVM ensemble using bisecting grid-based method is proposed(GBSVME).By hierarchically bisecting each grid into two volume-equal new grids,this approach use a new criterion to measure the significance among all grids. Then,using a random method to select some important grids to be further bisected.Therefore,the proposed approach can divide all data into some grids,and use all the grids as the input for training committee SVMs.Two experimental results show that the performance of GBSVME is better than that of mang other ensemble algorithms.