Feature selection is a process to select the best feature among huge number of features in
dataset, However, the problem in feature selection is to select a subset that give the better …

Classification analysis is widely adopted for healthcare applications to support medical
diagnostic decisions, improving quality of patient care, etc. A subset dataset of the extensive …

Feature selection refers to a critical preprocessing of machine learning to remove irrelevant
and redundant data. According to feature selection methods, sufficient samples are usually …

A comparison between several classification algorithms with feature extraction on real
dataset is presented. Principal Component Analysis (PCA) has been used for feature …

Data mining is a form of knowledge discovery essential for solving problems in a specific
domain. Classification is a technique used for discovering classes of unknown data. Various …

Feature selection aims to choose a feature subset that has the most discriminative
information from the original feature set. In practical cases, it is preferable to select a feature …

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 …

One may claim that the exponential growth in the amount of data provides great
opportunities for data mining. In many real world applications, the number of sources over …

At present, knowledge extraction from the given data set plays a significant role in all the
fields in our society. Feature selection process used to choose a few relevant features to …

Feature selection, both for supervised as well as for unsupervised classification is a relevant
problem pursued by researchers for decades. There are multiple benchmark algorithms …