  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.
