High-dimensional data classification model based on random projection and Bagging-support vector machine

Abstract

Aiming at the long training time when classifying high-dimensional data, a parallel classification model is proposed based on random projection and Bagging-support vector machine (SVM) to process high-dimensional data. The model first uses random projection to project the input data into the low-dimensional space. Then, we used the Bagging method to construct multiple training data subsets and used SVM to train the training subset in parallel and generate several subclassifiers. Finally, various classifiers vote to determine the category of the test sample. The model has been verified using two standard datasets. The experimental results show that the model can significantly improve the training speed and classification performance of high-dimensional data with little accuracy loss.