Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …

Today, many metaheuristics algorithms have been developed are inspired by the physical
phenomena or behaviors of natural creatures that are very effective in solving complex …

Massive data is generated as a result of technological innovations in various fields. Medical
data sets often have extremely complex dimensions with limited sample sizes. The …

For years, extensive research has been in the binarization of continuous metaheuristics for
solving binary-domain combinatorial problems. This paper is a continuation of a previous …

Wafer maps provide engineers with important information about the root causes of failures
during the semiconductor manufacturing process. Through the efficient recognition of the …

Evolving classifiers and especially evolving fuzzy classifiers have been established as a
prominent technique for addressing the recent demands in building classifiers in an …

In the diagnosis and treatment of cancer, cancer classification is a vital issue. Gene selection
is much needed to solve the high dimensionality issue in microarray data, small sample size …

The quantity of network attacks and the harm from them is constantly increasing, so the
detection of these attacks is an urgent task in the information security field. In this paper, we …

This paper concerns several important topics of the Symmetry journal, namely, pattern
recognition, computer-aided design, diversity and similarity. We also take advantage of the …

In this paper, we propose six methods for binarization of the swallow swarm optimization
(SSO) algorithm to solve the feature selection problem. The relevance of the selected feature …