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A Novel Class Imbalance Learning Method using Neural Networks

by K. Nageswara Rao, D. Rajya Lakshmi, T. Venkateswara Rao
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 7
Year of Publication: 2012
Authors: K. Nageswara Rao, D. Rajya Lakshmi, T. Venkateswara Rao
10.5120/ijais12-450594

K. Nageswara Rao, D. Rajya Lakshmi, T. Venkateswara Rao . A Novel Class Imbalance Learning Method using Neural Networks. International Journal of Applied Information Systems. 3, 7 ( August 2012), 31-38. DOI=10.5120/ijais12-450594

@article{ 10.5120/ijais12-450594,
author = { K. Nageswara Rao, D. Rajya Lakshmi, T. Venkateswara Rao },
title = { A Novel Class Imbalance Learning Method using Neural Networks },
journal = { International Journal of Applied Information Systems },
issue_date = { August 2012 },
volume = { 3 },
number = { 7 },
month = { August },
year = { 2012 },
issn = { 2249-0868 },
pages = { 31-38 },
numpages = {9},
url = { https://www.ijais.org/archives/volume3/number7/245-0594/ },
doi = { 10.5120/ijais12-450594 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:46:02.925148+05:30
%A K. Nageswara Rao
%A D. Rajya Lakshmi
%A T. Venkateswara Rao
%T A Novel Class Imbalance Learning Method using Neural Networks
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 3
%N 7
%P 31-38
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In Data mining and Knowledge Discovery hidden and valuable knowledge from the data sources is discovered. The traditional algorithms used for knowledge discovery are bottle necked due to wide range of data sources availability. Class imbalance is a one of the problem arises due to data source which provide unequal class i. e. examples of one class in a training data set vastly outnumber examples of the other class(es). In this paper, we present a new hybrid approach using neural networks to improve the class imbalance results. This algorithm provides a simpler and faster alternative by using multi perceptron back propagation neural network as base algorithm. We conduct experiments using eleven UCI data sets from various application domains using four base learners, and five evaluation metrics. Experimental results show that our method has shown good performance in terms of Area under the ROC Curve, F-measure, precision, TP rate and TN rate values than many existing class imbalance learning methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification class imbalance weighted sampling subset filtering CILNN

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