Background: Feature selection techniques are important factors for improving machine
learning models because they increase prediction accuracy and decrease the time to create …

The importance of software testing for quality assurance cannot be over emphasized. The
estimation of quality factors is important for minimizing the cost and improving the …

Algorithms for feature selection in predictive data mining for classification problems attempt
to select those features that are relevant, and are not redundant for the classification task. A …

As an important threat to public security, urban fire accident causes huge economic loss and
catastrophic collapse. Predicting and analyzing the interior rule of urban fire accident from its …

An efficient filter feature selection (FS) method is proposed in this paper, the SVM-FuzCoC
approach, achieving a satisfactory trade-off between classification accuracy and …

This paper presents a metaheuristic framework using Harmony Search (HS) with Genetic
Algorithm (GA) for gene selection. The internal architecture of the proposed model broadly …

In this paper, we present a semi-supervised rough set-based random forest gene selection
method for classification of data patterns. The proposed method tries to find the genes of …

Early detection of defective software components enables verification experts give much
time and allocate scare resources to the problem areas of the system under development …

The 'black box'nature of machine learning (ML) approaches makes it challenging to
understand how most artificial intelligence (AI) models make decisions. Explainable AI (XAI) …

One-Versus-All (OVA) classification is a classifier construction method where a k-class
prediction task is decomposed into k 2-class sub-problems. One base model is constructed …