One way to improve accuracy of a classifier is to use the minimum number of features. Many
feature selection techniques are proposed to find out the most important features. In this …

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 …

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 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 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 …

Feature selection is the process of selecting a subset of optimal features by removing
redundant and irrelevant features. In supervised learning, feature selection process uses …

Feature selection is the task of selecting a small subset from original features that can
achieve maximum classification accuracy. This subset of features has some very important …

A novel method of feature selection combined with sample selection is proposed to select
discriminant features in this paper. Based on support vector machine trained on training set …

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 …

Features play a very important role in the task of pattern classification. Consequently, the
selection of suitable features is necessary as most of the raw data might be redundant or …