This paper introduces a new technique for feature selection and illustrates it on a real data
set. Namely, the proposed approach creates subsets of attributes based on two criteria:(1) …

The purpose of the publication is to analyse popular classification algorithms in machine
learning. The following classifiers were studied: Naive Bayes Classifier, Decision Tree and …

Finding a subset of features from a large dataset is a problem that arises in many fields of
study. Since the increasing number of features has extended the computational cost of a …

Feature selection is an important data preprocessing step in machine learning and data
mining, such as classification tasks. Research on feature selection has been extensively …

Objectives: To make a comparative study about different classification techniques of data
mining. Methods: In this paper some data mining techniques like Decision tree algorithm …

One major component of machine learning is feature analysis which comprises of mainly
two processes: feature selection and feature extraction. Due to its applications in several …

Objectives: The most appropriate classifier selections for the particular data sets were
generally found harder. Therefore, in this study various existing classifiers have been …

The process of Feature selection in machine learning involves the reduction in the number
of features (genes) and similar activities that results in an acceptable level of classification …

Feature selection is an indispensable step in the data preprocessing stage of data mining
and pattern recognition. In some numerical small sample data, it is often high dimensional in …

Feature selection refers to a problem to select a subset of features which are most optimal
for intended tasks. As one of well-known feature selection methods, clustering features into …