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

Datasets often include excessive or irrelevant data that affect the performance and
complexity of the machine learning model. Feature selection is one of the most effective …

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

The presence of a large number of irrelevant features degrades the classifier accuracy,
reduces the understanding of data, and increases the overall time needed for training and …

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 …

With the rapid development of information techniques, the dimensionality of data in many
application domains, such as text categorization and bioinformatics, is getting higher and …

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 …

Data mining is an inevitable step in knowledge discovery and it helps discovering hidden
and useful patterns among data. These days, the number of stored attributes for each entity …