Text of abstract Logistic regression analysis is an effective approach to the classification
problem. However, it may lead to high misclassification rate in real decision procedures …

Rough set theory has been used extensively in fields of complexity, cognitive sciences, and
artificial intelligence, especially in numerous fields such as expert systems, knowledge …

In real cases, missing values tend to contain meaningful information that should be acquired
or should be analyzed before the incomplete dataset is used for machine learning tasks. In …

In recent years, gene expression analysis has become crucial in studying microarray and
provides several techniques related to computational biology and bioinformatics. This article …

Research in the area of fuzzy–rough set theory, and its application to feature or attribute
selection in particular, has enjoyed much attention in recent years. Indeed, with the growth of …

Attribute reduction (AR) plays an important role in reducing irrelevant and redundant domain
attributes, while maintaining the underlying semantics of retained ones. Based on Earth …

Feature selection has gained significant attention, with a focus on removing redundant or
irrelevant features to improve subsequent machine learning tasks. The fuzzy rough set is …

This paper aims to design distraction descriptor, elicited through the object variation, to
refine the granular knowledge incrementally, using the proposed probability-based …

In this article, we propose a novel type-2 fuzzy multigranulation-based SVM model for gene
expression pattern classification on human breast cancer dataset. Firstly, a type-2 fuzzy …

This paper proposes an Incremental Fuzzy‐Rough Nearest Neighbour (IncFRNN) technique
for biometric authentication modelling using feature extracted visual evoked. Only small …