Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …

Abstract Support Vector Machines (SVM) are an efficient alternative for supervised
classification. In the soft margin SVM model, two different objectives are optimized and the …

Feature selection is an important data processing method to reduce dimension of the raw
datasets while preserving the information as much as possible. In this paper, an enhanced …

Feature selection (FS) is an important data pre-processing technique in classification. In
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …

Availability of suitable and validated data is a key issue in multiple domains for
implementing machine learning methods. Higher data dimensionality has adverse effects on …

Recently, convolutional neural networks (CNNs) have achieved great success in the field of
artificial intelligence, including speech recognition, image recognition, and natural language …

With the development of deep learning, the design of an appropriate network structure
becomes fundamental. In recent years, the successful practice of Neural Architecture Search …

Scheduling has an immense effect on various areas of human lives, be it though its
application in manufacturing and production industry, transportation, workforce allocation, or …

Abstract Information fusion refers to derive an overall precise description of data by using
certain fusion technique for utilizing the complementary information from multiple sources of …

This study relies on using a Moth–Flame Optimization (MFO) method as a search algorithm
and a Decision Tree (DT) as an evaluation algorithm to generate an efficient feature subset …