Feature selection methods are deployed in machine-learning algorithms for reducing the
redundancy in the dataset and to increase the clarity in the system models without loss of …

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 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 …

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 …

Feature selection is the process of eliminating features from the data set that are irrelevant
with respect to the task to be performed. Feature selection is important for many reasons …

Classification of data crosses different domains has been extensively researched and is one
of the basic methods for distinguishing one from another, as we need to know which belongs …

Hand gestures are widely used in human-to-human and human-to-machine communication.
Therefore, hand gesture recognition is a topic of great interest. Hand gesture recognition is …

Feature selection has become interest to many research areas which deal with machine
learning and data mining, because it provides the classifiers to be fast, cost-effective, and …

Feature selection has been widely applied in many areas such as classification of spam
emails, cancer cells, fraudulent claims, credit risk, text categorisation and DNA microarray …

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 …