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 …

This paper proposes one method of feature selection by using Bayes' theorem. The purpose
of the proposed method is to reduce the computational complexity and increase the …

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 indispensable pre-processing technique for selecting more relevant
features and eradicating the redundant attributes. Finding the more relevant features for the …

This paper proposes a feature selection method that combines various feature selection
techniques. Feature selection has been realized as one of the most important processes in …

The purpose of the feature selection is to eliminate insignificant features from entire dataset
and simultaneously to keep the class discriminatory information for classification problems …

Feature selection is used to improve the efficiency of learning algorithms by finding an
optimal subset of features. However, most feature selection techniques can handle only …

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 …

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 plays an important role in pattern classification. Its purpose is to remove
redundant features from data set as many as possible. The presence of useless features …