Efficient Divide-And-Conquer Classification Based on Feature-Space Decomposition

TL;DR

This paper introduces a novel divide-and-conquer classification method based on feature space decomposition, improving accuracy on large-scale datasets by reducing error rates compared to existing fast SVM solvers.

Contribution

It proposes a new DC approach on feature spaces with a three-step process, enhancing class separability and classification accuracy for large datasets.

Findings

Error rates decreased by 10.53% on RCV1 dataset

Error rates decreased by 7.53% on covtype dataset

Outperforms state-of-the-art fast SVM solvers

Abstract

This study presents a divide-and-conquer (DC) approach based on feature space decomposition for classification. When large-scale datasets 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 work proposes a novel DC approach on feature spaces consisting of three steps. Firstly, 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 outcomes of local classifiers are fused together to generate the final classification results. Experiments on large-scale datasets are carried out for performance evaluation. The results show that the error rates of the proposed DC method decreased comparing with the state-of-the-art fast SVM solvers, e.g., reducing error rates by 10.53% and 7.53% on RCV1 and covtype datasets respectively.