Research on Parallel SVM Algorithm Based on Cascade SVM
This work addresses the trade-off between speed and accuracy in parallel SVM training for machine learning practitioners, but it is incremental as it builds directly on CSVM.
The paper tackles the accuracy error in Cascade SVM (CSVM) for parallel training by proposing a Balanced Cascade SVM (BCSVM) algorithm that balances sample proportions in subsets, resulting in a reduction of accuracy error from 1% to 0.1% on two datasets.
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.