Feature Selection (FS) is an imperative issue in data mining and machine learning. It is an
inevitable task to shorter the number of features presented in the initial data set for better …

Selection of strong features is crucial problem in machine learning. It is also considered as
an inescapable exercise to minimize the number of variables available in the primary feature …

Feature selection goal is to get rid of redundant and irrelevant features. The problem of
feature subset selection is that of finding a subset of the original features of a dataset, such …

Data mining methods are frequently applied in the framework of data classification. Under
data mining methods, feature selection (FS) algorithms are essential for dealing with various …

Feature selection (FS)(or feature dimensional reduction, or feature optimization) is an
essential process in pattern recognition and machine learning because of its enhanced …

Abstract Machine learning has become an integral part of our life in today's world. Machine
learning when applied to real-world applications suffers from the problem of high …

Prediction plays a vital role in decision making. Correct prediction leads to right decision
making to save the life, energy, efforts, money and time. The right decision prevents physical …

Feature selection is an important data preprocessing step which is performed before a
learning algorithm is applied. The issue that has to be taken into consideration when …

With the advent of big data era and the rapid improvement of raw data scale, feature
selection, as the basis and critical technologies for data mining, plays an increasingly …

Background/Objectives: A novel feature selection framework using minimum variance
method is proposed. The purpose of the proposed method is to reduce the computational …