Feature selection, both for supervised as well as for unsupervised classification is a relevant
problem pursued by researchers for decades. There are multiple benchmark algorithms …

Feature selection is an important task in many fields such as statistics and machine learning.
It aims at preprocessing step that include removal of irrelevant and redundant features and …

Features selection is the process of choosing the relevant subset of features from the high‐
dimensional dataset to enhance the performance of the classifier. Much research has been …

Purpose The purpose of this paper is to present an empirical study on the effect of two
synthetic attributes to popular classification algorithms on data originating from student …

A comparative study of four well-known supervised machine learning techniques namely;
Decision Tree, K-Nearest-Neighbor, Artificial-Neural-Network and Support Vector Machine …

Feature selection remains one of the most important steps for usability of a model for both
supervised and unsupervised classification. For a dataset, with n features, the number of …

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

In this paper, the efficacy of seven data classification methods; Decision Tree (DT), k-
Nearest Neighbor (k-NN), Logistic Regression (LogR), Naïve Bayes (NB), C4. 5, Support …

As a criterion of feature selection, F-score does not consider the influence of the different
measuring dimensions on the importance of different features. To evaluate the …