Abstract Machine Learning (ML) techniques are becoming an invaluable support for network
intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats …

Feature selection has gained much consideration from scholars working in the domain of
machine learning and data mining in recent years. Feature selection is a popular problem in …

Many bankruptcy prediction models for small and medium-sized enterprises (SMEs) are built
using accounting-based financial ratios. This study proposes a bankruptcy prediction model …

Feature selection is an important data preprocessing method. This paper studies a new multi-
objective feature selection approach, called the Binary Differential Evolution with self …

With the growth of online shopping, Credit Card Fraud (CCF) comes out as a serious
menace. For this end, the automatic and real-time fraud detection field calls for several …

The advancements in communication technologies and ubiquitous accessibility to a wide
array of services has opened many challenges. Growing numbers of cyberattacks show that …

This article presents a comprehensively state-of-the-art investigation of the engineering
applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based …

Modelling consumer credit risk is a crucial task for banks and non-bank financial institutions
to support decision-making on granting loans. To model the overall credit risk of a consumer …

Researchers are motivated to build effective Intrusion Detection Systems because of the
implications of malicious actions in computing, communication, and cyber–physical systems …

Feature selection wrapper methods are powerful mechanisms for reducing the complexity of
prediction models while preserving and even improving their precision. Meta-heuristic …