Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

Abstract

Twin support vector machine (TWSVM) is a new machine learning method, as opposed to solving a single quadratic programming problem in support vector machine (SVM), which generates two nonparallel hyperplanes by solving two smaller size quadratic programming problems. However, the TWSVM obtains the final classifier by giving the same importance to all training samples which may be important for classification performance. In order to address this problem, in this paper, we propose a novel entropy-based fuzzy twin bounded support vector machine (EFTBSVM) for binary classification problems. By considering the fuzzy membership value for each sample and assigning it based on the entropy value, the samples with higher class certainty are assigned to relatively larger fuzzy membership. In addition, the proposed EFTBSVM not only maintains the superior characteristics of the TWSVM but also exploits the structural risk minimization principle by introducing a regularization term. The experimental results achieved on synthetic datasets and benchmark datasets illustrate the effectiveness of the proposed method.

Smooth twin bounded support vector machine with pinball loss

“…It can be seen that the left and right derivatives of the pinball loss function at zero are not equal, that is, the function is not differentiable at zero. To this end, a smooth function ϕ τ (u, ε) is used to approximate it by using (16) and (17), where ε is a sufficiently small parameter, and the function ϕ τ (u, ε) is shown in (18):…”

“…It further improves the generalization performance of the TWSVM. In addition, researchers have proposed different twin support vector machines by exploring the structures of datasets and the different roles of samples [11][12][13][14][15][16][17][18].…”

Multi-owner Secure Encrypted Search Using Searching Adversarial Networks

“…Probabilistic learning is employed to obtain MLSB-Tree, which is ordered in a maximum probability. The experimental evaluation shows that MLSB-Tree-based search is faster and more stable compare with related works [6,16]; (3) flexible dynamic system maintenance based on balanced index forest (BIF): Using unsupervised learning [3,10] to design a fast index clustering algorithm to classify all indexes into multiple index partitions, and a corresponding balanced index tree is constructed for each index partition, thus all index trees form BIF. Owing to BIF is distributed, it only needs to maintain the corresponding index partition without touching all indexes in dynamic system maintenance, which improves the efficiency of index update operations and introduces low overhead on the computation, communication and storage.…”