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Dimensionality Reduction: An Effective Technique for Feature Selection

by Swati A Sonawale, Roshani Ade
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 3
Year of Publication: 2015
Authors: Swati A Sonawale, Roshani Ade
10.5120/20535-2893

Swati A Sonawale, Roshani Ade . Dimensionality Reduction: An Effective Technique for Feature Selection. International Journal of Computer Applications. 117, 3 ( May 2015), 18-23. DOI=10.5120/20535-2893

@article{ 10.5120/20535-2893,
author = { Swati A Sonawale, Roshani Ade },
title = { Dimensionality Reduction: An Effective Technique for Feature Selection },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 3 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number3/20535-2893/ },
doi = { 10.5120/20535-2893 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:58:20.955864+05:30
%A Swati A Sonawale
%A Roshani Ade
%T Dimensionality Reduction: An Effective Technique for Feature Selection
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 3
%P 18-23
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For knowledge gaining the dimensionality reduction is a significant technique. It has been observed that most of the time dataset is multidimensional and larger in size. When we are using same dataset for classification it may create wrong results and it may also requires more requirements in terms of storage as well as processing capability. Most of the features present are redundant, inconsistent and degrade the performance. To increase the effectiveness of classification these duplicate and inconsistent features must be removed. In this research we have introduced a new method for dealing with the problem of dimensionality reduction. By reducing the unrelated (irrelevant) and unnecessary features related to data, or by means of effectively merging original features to produce a smaller set of feature with more discriminative control, dimensionality reduction methods convey the instant effects of rapid the data mining algorithms, better performance, and increase in unambiguous of data model

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Index Terms

Computer Science
Information Sciences

Keywords

Dimension reduction Fuzzy ARTMAP Feature selection Feature extraction Supervised and Unsupervised techniques semi-supervised techniques.

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