Adaptive memetic algorithm enhanced with data geometry analysis to select training data for SVMs

Support vector machines (SVMs) are one of the most popular and powerful machine learning techniques, but suffer from a significant drawback of the high time and memory complexities of their training. This issue needs to be endured especially in the case of large and noisy datasets. In this paper, we propose a new adaptive memetic algorithm (PCA²MA) for selecting valuable SVM training data from the entire set. It helps improve the classifier score, and speeds up the classification process by decreasing the number of support vectors. In PCA²MA, a population of reduced training sets undergoes the evolution, which is complemented by the refinement procedures. We propose to exploit both a priori information about the training set-extracted using the data geometry analysis-and the knowledge attained dynamically during the PCA²MA execution to enhance the refined sets. Also, we introduce a new adaptation scheme to control the pivotal algorithm parameters on the fly, based on the current search state. Extensive experimental study performed on benchmark, real-world, and artificial datasets clearly confirms the efficacy and convergence capabilities of the proposed approach. We demonstrate that PCA²MA is highly competitive compared with other state-of-the-art techniques.