Efficient Divide-and-Conquer Classification Based on Parallel Feature-Space Decomposition for Distributed Systems

This paper presents a divide-and-conquer (DC) approach based on feature-space decomposition for classification. When large-scale data sets are present, typical approaches usually employed truncated kernel methods on the feature space or DC approaches on the sample space. However, this did not guarantee separability between classes, owing to overfitting. To overcome such problems, this paper proposes a novel DC approach on feature spaces consisting of three steps. First, we divide the feature space into several subspaces using the decomposition method proposed in this paper. Subsequently, these feature subspaces are sent into individual local classifiers for training. Finally, the outcome of local classifiers are fused together to generate the final classification results. We also propose a Cascade-TRBFKRR classifier to reweight training samples for data refinement. Experiments on large-scale data sets are carried out for performance evaluation. The results show that the error rates of the proposed DC method decreased compared with the state-of-the-art fast support vector machine solvers, e.g., reducing error rates by 10.53% and 7.53% on RCV1 and covtype data sets, respectively.