Bootstrap Sampling Based Data Cleaning and Maximum Entropy SVMs for Large Datasets

Support Vector Machines (SVMs) is a popular machine learning algorithm based on Statistical Learning Theory (SLT). However, traditional solutions suffer from O(n²) time complexity. In this paper, a novel two-stage informative pattern abstraction algorithm is proposed. The first stage of the algorithm is data cleaning based on bootstrap sampling. A bundle of weak SVM classifiers are trained based on the sampled small datasets. Training data correctly classified by all the weak classifiers are cleaned. In the second stage, to further improve performance of final classifier and reduce training time, two novel informative pattern extraction algorithms based on entropy maximization SVMs are proposed. Empirical studies show our approach is effective in reducing size of training datasets and the computational cost, outperforming the state-of-the-art SVM training algorithms PEGASOS, RSVM and LIBLINEAR SVM with comparable classification accuracy.