A Survey on Various Feature Selection Methodologies

The process of selecting features is important process in machine learning; this is method of selecting a subset of relevant/ significant variables and features. Feature selection is applicable in multiple areas such as anomaly detection, Bioinformatics, image processing, etc. where high dimensional data is generated. Analysis and classification of such big data is time consuming. Feature set selection is generally user for: to simplify model data set, reducing over fitting, increase efficiency of classifier. In this paper we have analyzed various techniques for extraction of features and feature subset collection. The main objective behind this research was to find a better algorithm for extraction of features and feature subset collection with efficiency. Subsequently, several methods for the extraction and selection of features have been suggested to attain the highest relevance.