Class imbalance and noise present significant challenges in numerous real-world
classification tasks. The prevalence of an uneven distribution of samples typically results in a …

This paper proposes a method of blind modulation format identification using decision tree
twin support vector machine classifier trained with the features extracted from the high-order …

Alzheimer's disease (AD) is an irreversible neurological degenerative disorder
characterized by the deterioration of brain cells resulting in cognitive impairment. There is a …

As one of the classical machine learning algorithms, twin support vector machine (TWSVM)
can construct two nonparallel hyperplanes, which keeps the hyperplane close to points of …

In recent years, gesture recognition in video sequences has aroused growing interest in the
fields of computer vision and behavioral understanding, for example in the control of robots …

A new classification model, the fuzzy hybrid twin support vector machine (TWSVM), namely
FHTWSVM, is proposed by combining the fuzzy TWSVM and the hypersphere support vector …

In the last decade, twin support vector machine (TWSVM) classifiers have achieved
considerable emphasis on pattern classification tasks. However, the TWSVM formulation still …

Clustering is an unsupervised learning algorithm and it is widely used in machine learning.
Twin support vector clustering (TWSVC) is a new plane-based clustering algorithm, which …

In this paper, we propose a semi-supervised classifier termed as Fast Laplacian Twin
Support Vector Machine (FL ap− TWSVM) with an objective to reduce the requirement of …

Among the extensions of twin support vector machine (TSVM), some scholars have utilized
K-nearest neighbor (KNN) graph to enhance TSVM's classification accuracy. However …