Consensus Proximal Support Vector Machine for Classification Problems with Sparse Solutions

Abstract

Classification problem is the central problem in machine learning. Support vector machines (SVMs) are supervised learning models with associated learning algorithms and are used for classification in machine learning. In this paper, we establish two consensus proximal support vector machines (PSVMs) models, based on methods for binary classification. The first one is to separate the objective functions into individual convex functions by using the number of the sample points of the training set. The constraints contain two types of the equations with global variables and local variables corresponding to the consensus points and sample points, respectively. To get more sparse solutions, the second one is l 1–l 2 consensus PSVMs in which the objective function contains an l1-norm term and an l2-norm term which is responsible for the good classification performance while l1-norm term plays an important role in finding the sparse solutions. Two consensus PSVMs are solved by the alternating direction method of multipliers. Furthermore, they are implemented by the real-world data taken from the University of California, Irvine Machine Learning Repository (UCI Repository) and are compared with the existed models such as l1-PSVM, l p -PSVM, GEPSVM, PSVM, and SVM-light. Numerical results show that our models outperform others with the classification accuracy and the sparse solutions.