Feature selection is an important preprocessing process in machine learning. It selects the
crucial features by removing irrelevant features or redundant features from the original …

Imbalanced datasets are prominent in real-world problems. In such problems, the data
samples in one class are significantly higher than in the other classes, even though the other …

Intuitionistic fuzzy (IF) set theory combined with twin support vector machines (TSVM) has
shown highly advantageous performance in robust and fast classification. However, the …

Truncated loss functions are robust to class noise and outliers. A robust twin bounded
support vector machine is proposed in this paper that truncates the growth of its loss …

Electroencephalogram (EEG) signals have been generally utilized for diagnostic systems.
Nowadays artificial intelligence-based systems have been proposed to classify EEG signals …

Due to the increasing popularity of support vector machine (SVM) and the introduction of
Universum, many variants of SVM along with Universum such as Universum support vector …

Electroencephalogram (EEG) have been widely used for the diagnosis of neurological
diseases like epilepsy and sleep disorders. Support vector machines (SVMs) are widely …

Classifying individuals with neurological disorders and healthy subjects using EEG is a
crucial area of research. The current feature extraction approach focuses on the frequency …

Neuroscience has made astonishing advancements in understanding the human brain with
the help of Brain-Computer Interface. Recent contributions in the field of Artificial Intelligence …

In data analysis, when attempting to solve classification problems, we may encounter a large
number of features. However, not all features are relevant for the current classification, and …