Ensemble method for classification of high-dimensional data

Ensemble methods, also known as classifier combination were often used to improve the performance of classification. Growing problem of data dimensionality makes a various challenges for supervised learning. Generally used classification methods such as decision tree, neural network and support vector machines were difficult to be directly applied on high-dimensional datasets. In this paper, we proposed an ensemble method for classification of high-dimensional data, with each classifier constructed from a different set of features determined by partition of redundant features. In our method, the redundancy of features was considered to divide the original feature space. Then, each generated feature subset was trained by support vector machine and the results of each classifier were combined by the majority voting method. The efficiency and effectiveness of our method were demonstrated through comparisons with other ensemble techniques, and the results showed that our method outperformed other methods.