Review on: Twin Support Vector Machines

Abstract

Twin support vector machine (TWSVM), an useful extension of the traditional SVM, becomes the current researching hot spot in machine learning during the last few years. For the binary classification problem, the basic idea of TWSVM is to seek two nonparallel proximal hyperplanes such that each hyperplane is closer to one of the two classes and is at least one distance from the other. TWSVM has lower computational complexity and better generalization ability, therefore in the last few years it has been studied extensively and developed rapidly. Considering the many variants of TWSVM, a systematic survey is needed and helpful to understand and use this family of data mining techniques more easily. The purpose of this paper is to closely review TWSVMs and provide an insightful understanding of current developments, at the same time point out their limitations and highlight the major opportunities and challenges, as well as potential important research directions.

Optimizing Alzheimer's disease prediction using the nomadic people algorithm

“…Starting with a concept of nomadic people and their mode of existence, this section discussed the fundamentals of NPO; this is used as the basis for the proposed method. NPO is the algorithm associated with social aspects and is developed around considering the movement of nomads in search of food [32]. The NPO algorithm can easily deal with large scale problems.…”

Twin Neural Networks for the classification of large unbalanced datasets

“…The harmonic mean between the positive prediction value and true positive rate is called the F-measure, which is computed as given by Eqn. (23).…”

“…Several extensions of the TWSVM have been proposed in the literature. Reviews of the Twin SVM and its extensions have appeared in AI Review (2014) [6], the Annals of Data Science (2014) [23], and the Egyptian Informatics Journal (2015) [24]. Some of the extensions include Knowledge based Least Squares TWSVM [1], Margin Based TWSVM [20], -TWSVM [22], Twin Parametric Margin Classifier [17], Least-squares Twin Parametric-Margin Classifier [21], Twin Support Vector Hypersphere Classifier [18], Least-squares Twin Support Vector Hypersphere Classifier [14], Structural TWSVM [19] among others.…”

Twin Support Vector Machine Method for Identification of Wiener Models

“…Lately, many extensions of TSVM classifier have been proposed to improve its performance in certain aspects. A TSVM classifier has been extended to put the TSVM to multitask learning [16]. A generalized framework of TSVM for learning from labeled and unlabeled data was investigated by [17,18].…”