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In this paper, we propose a novel least squares twin parametric-margin support vector machine (TPMSVM) for binary classification, called LSTPMSVM for short. LSTPMSVM attempts to solve two modified primal problems of TPMSVM, instead of two dual problems usually solved. The solution of the two modified primal problems reduces to solving just two systems of(More)
Laplacian twin support vector machine (Lap-TSVM) is a state-of-the-art nonparallel-planes semi-supervised classifier. It tries to exploit the geometrical information embedded in unlabeled data to boost its generalization ability. However, Lap-TSVM may endure heavy burden in training procedure since it needs to solve two quadratic programming problems (QPPs)(More)
In this letter, we propose an improved version of generalized eigenvalue proximal support vector machine (GEPSVM), called IGEPSVM for short. The main improvements are 1) the generalized eigenvalue decomposition is replaced by the standard eigenvalue decomposition, resulting in simpler optimization problems without the possible singularity. 2) An extra(More)
Both genetic and epigenetic alterations can control the progression of cancer. Genetic alterations are impossible to reverse, while epigenetic alterations are reversible. This advantage suggests that epigenetic modifications should be preferred in therapy applications. DNA methyltransferases and histone deacetylases have become the primary targets for(More)
Keywords: Imbalanced data classification Twin support vector machine Weighted twin support vector machine Lagrangian functions Quadratic cost functions a b s t r a c t In this paper, we propose an efficient weighted Lagrangian twin support vector machine (WLTSVM) for the imbalanced data classification based on using different training points for(More)