Research on Parallel SVM Algorithm Based on Cascade SVM

TL;DR

This paper introduces the Balanced Cascade SVM (BCSVM) algorithm, which improves the accuracy of parallel SVM training by balancing sample proportions, significantly reducing error compared to traditional Cascade SVM.

Contribution

The paper proposes BCSVM, a novel method that enhances accuracy in parallel SVM training by balancing sample distribution during grouping.

Findings

BCSVM reduces accuracy error from 1% to 0.1%.

Experimental results confirm improved model accuracy.

BCSVM maintains efficiency while enhancing accuracy.

Abstract

Cascade SVM (CSVM) can group datasets and train subsets in parallel, which greatly reduces the training time and memory consumption. However, the model accuracy obtained by using this method has some errors compared with direct training. In order to reduce the error, we analyze the causes of error in grouping training, and summarize the grouping without error under ideal conditions. A Balanced Cascade SVM (BCSVM) algorithm is proposed, which balances the sample proportion in the subset after grouping to ensure that the sample proportion in the subset is the same as the original dataset. At the same time, it proves that the accuracy of the model obtained by BCSVM algorithm is higher than that of CSVM. Finally, two common datasets are used for experimental verification, and the results show that the accuracy error obtained by using BCSVM algorithm is reduced from 1% of CSVM to 0.1%, which is reduced by an order of magnitude.