With the extensive applicability of machine learning classification algorithms to a wide
spectrum of domains, feature selection (FS) becomes a relevant data preprocessing …

One of the major aspects of any classification process is selecting the relevant set of features
to be used in a classification algorithm. This initial step in data analysis is called the feature …

Feature selection (FS) is a critical step in data mining, and machine learning algorithms play
a crucial role in algorithms performance. It reduces the processing time and accuracy of the …

This paper proposes two wrapper based feature selection approaches, which are single
feature ranking and binary particle swarm optimisation (BPSO) based feature subset …

For the first time, the ensemble feature selection is modeled as a Multi-Criteria Decision-
Making (MCDM) process in this paper. For this purpose, we used the VIKOR method as a …

A traditional feature selection of filters evaluates the importance of a feature by using a
particular metric, deducing unstable performances when the dataset changes. In this paper …

A filter feature selection algorithm is developed and its performance tested. In the initial step,
the algorithm dichotomizes the dataset then separately computes the association between …

Classification algorithms and their preprocessing operations usually performs on feature
selection on homogeneous or heterogeneous attributes, binary or multi-class labels …

We presented a comparison between several feature ranking methods used on two real
datasets. We considered six ranking methods that can be divided into two broad categories …

This paper describes a method for the identification of informative variables in an information
system with discrete decision variables. It is targeted specifically towards the discovery of …