Support vector machine ensemble based on independent component analysis and fuzzy kernel clustering

In order to improve the generalization performance of support vector machine (SVM), a support vector machine ensembling method based on independent component analysis (ICA) and fuzzy kernel clustering (FKC) was proposed. The ICA emphasizes the independence between the data characteristics and can effectively obtain a series of independent features, the performance of single SVM can be improved when the SVM was trained on these independent features; The FKC method uses kernel function to expand the feature space, so that the clustering is more accurate and the diversity between sub-SVMs can also be guaranteed. Simulation results on UCI datasets show that the proposed ensembling method can improve the classification precision of SVM and make the ensemble SVM has better generalization property.