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Reseach Article

Enhancing BPN Performance using GA Identified Significant Features: A Case Study for Categorization of Heart Statlog Dataset

Published on December 2013 by Asha Gowda Karegowda
International Conference on Computing and information Technology 2013
Foundation of Computer Science USA
IC2IT - Number 1
December 2013
Authors: Asha Gowda Karegowda
c8805774-072c-4145-a523-8c15bf5cad28

Asha Gowda Karegowda . Enhancing BPN Performance using GA Identified Significant Features: A Case Study for Categorization of Heart Statlog Dataset. International Conference on Computing and information Technology 2013. IC2IT, 1 (December 2013), 1-4.

@article{
author = { Asha Gowda Karegowda },
title = { Enhancing BPN Performance using GA Identified Significant Features: A Case Study for Categorization of Heart Statlog Dataset },
journal = { International Conference on Computing and information Technology 2013 },
issue_date = { December 2013 },
volume = { IC2IT },
number = { 1 },
month = { December },
year = { 2013 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/ic2it/number1/14383-1301/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Computing and information Technology 2013
%A Asha Gowda Karegowda
%T Enhancing BPN Performance using GA Identified Significant Features: A Case Study for Categorization of Heart Statlog Dataset
%J International Conference on Computing and information Technology 2013
%@ 0975-8887
%V IC2IT
%N 1
%P 1-4
%D 2013
%I International Journal of Computer Applications
Abstract

Feature selection is an essential pre-processing method to remove irrelevant and redundant data. This paper presents the development of a model for classifying Heart Statlog. The model consists of two stages. In the first stage, genetic algorithm (GA) is used as random search method with Correlation based feature selection as fitness function for identifying significant features. The second stage a fine tuned classification is done using back propagation neural network using GA identified feature subset elicited in the first stage. Experimental results signify that the feature subset identified by the proposed filter when given as input to Back propagation neural network classifier, leads to enhanced classification accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature Selection Filter Approach Genetic Algorithm Correlation Based Feature Selection Back Propagation Neural Network.