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Reseach Article

Literature Review of Feature Selection for Mining Tasks

by Muhammad Shakil Pervez, Dewan Md. Farid
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 21
Year of Publication: 2015
Authors: Muhammad Shakil Pervez, Dewan Md. Farid
10.5120/20462-2829

Muhammad Shakil Pervez, Dewan Md. Farid . Literature Review of Feature Selection for Mining Tasks. International Journal of Computer Applications. 116, 21 ( April 2015), 30-33. DOI=10.5120/20462-2829

@article{ 10.5120/20462-2829,
author = { Muhammad Shakil Pervez, Dewan Md. Farid },
title = { Literature Review of Feature Selection for Mining Tasks },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 21 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 30-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number21/20462-2829/ },
doi = { 10.5120/20462-2829 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:57:47.264724+05:30
%A Muhammad Shakil Pervez
%A Dewan Md. Farid
%T Literature Review of Feature Selection for Mining Tasks
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 21
%P 30-33
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

During past few decades, researchers worked on data preprocessing techniques for the datasets. Data preprocessing techniques are needed, where the data are prepared for mining. The performance of data mining algorithms in most cases depends on dataset quality, since low-quality training data may lead to the construction of over?tting or fragile classi?ers. Also, scientists worked on data mining areas in both algorithms section and conceptions practice section. But for better results they always used the combined or embedded or hybrid approaches. Scientists used different classifiers in different ways and also got their smoother results by arranging some modification in the algorithms. In this paper we shall describe all possible areas of attribute selection and reduction techniques. Feature selection algorithms broadly fall into three categories: ?lter models, wrapper models and hybrid models. Practically, scientists do the tasks in two stages for obtaining accuracy and that is, they firstly select the features and then reduce the dimensionality of feature vectors with classifiers through learning. Some promising approaches are indicated here and particular concentration is dedicated to describe different methods from raw level to experts, so that in future one can get significant instruction for further analysis.

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Index Terms

Computer Science
Information Sciences

Keywords

Embedded hybrid ?lter wrapper classifiers