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Reseach Article

Dimensionality Reduction by Cascading Mutual Correlation with Symbolic Approach

by Veerabhadrappa
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 7
Year of Publication: 2016
Authors: Veerabhadrappa
10.5120/ijca2016909362

Veerabhadrappa . Dimensionality Reduction by Cascading Mutual Correlation with Symbolic Approach. International Journal of Computer Applications. 140, 7 ( April 2016), 5-8. DOI=10.5120/ijca2016909362

@article{ 10.5120/ijca2016909362,
author = { Veerabhadrappa },
title = { Dimensionality Reduction by Cascading Mutual Correlation with Symbolic Approach },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 7 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 5-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume140/number7/24604-2016909362/ },
doi = { 10.5120/ijca2016909362 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:41:37.383215+05:30
%A Veerabhadrappa
%T Dimensionality Reduction by Cascading Mutual Correlation with Symbolic Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 7
%P 5-8
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we propose a novel cascading approach, by cascading the feature selection method using mutual correlation with this symbolic approach. In the symbolic approach, the new dimensionality reduction method through transformation of features into symbolic data using the property of collinearity and variance based cumulative sum of features is used. The feature values are transformed into line segments and thus reduced to two symbolic features namely, number of line segments and average slope of the line segments. In addition the first and last feature values are also considered to distinguish the samples with the same average slope values. In this proposed approach of cascading the feature selection method using mutual correlation with this symbolic approach, the entire feature set is reduced to only 4 features. Experimental results on the standard datasets WDBC, WBC, CORN SOYANEAN and WINE shows that the proposed methods achieve better classification performance with negligible time.

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Index Terms

Computer Science
Information Sciences

Keywords

Symbolic features mutual correlation Extraction of lines Cumulative sum of features.