Abstract: Cascade support vector machine (CSVM) divides the dataset into groups and trains them in parallel, greatly reducing training time and memory usage. However, the accuracy of the model obtained using this method has certain errors compared to direct training. In order to reduce the error, the reasons for the error caused by grouping training are analyzed, and the ideal grouping without error is summarized. A balanced cascade support vector machine (BCSVM) algorithm is proposed. The algorithm balances the sample proportions in the sub-datasets after grouping, ensuring that the sample proportions in the sub-datasets are the same as those in the original dataset. It adjusts the parameter values during grouping training to obtain more support vectors, thereby reducing the possibility of global support vector loss. At the same time, researchers discussed the effectiveness of BCSVM algorithm and demonstrated that models obtained using this algorithm have better performance in prediction accuracy than those obtained using random grouping CSVM. Finally, multiple common datasets are used for experimental verification, and the results show that the accuracy error obtained by training using the BCSVM algorithm is reduced from 1% to about 0.1%, i.e., by one order of magnitude.

Key words: parallel computing, support vector machine, chunking, balanced subset, parameter scaling