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Reseach Article

Recursive Ensemble Approach for Incremental Learning of Non-Stationary Imbalanced Data

by Pradnya A. Jain, Roshani Raut (ade), P. R. Deshmukh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 17
Year of Publication: 2014
Authors: Pradnya A. Jain, Roshani Raut (ade), P. R. Deshmukh
10.5120/17279-7732

Pradnya A. Jain, Roshani Raut (ade), P. R. Deshmukh . Recursive Ensemble Approach for Incremental Learning of Non-Stationary Imbalanced Data. International Journal of Computer Applications. 98, 17 ( July 2014), 41-45. DOI=10.5120/17279-7732

@article{ 10.5120/17279-7732,
author = { Pradnya A. Jain, Roshani Raut (ade), P. R. Deshmukh },
title = { Recursive Ensemble Approach for Incremental Learning of Non-Stationary Imbalanced Data },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 17 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number17/17279-7732/ },
doi = { 10.5120/17279-7732 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:26:28.871977+05:30
%A Pradnya A. Jain
%A Roshani Raut (ade)
%A P. R. Deshmukh
%T Recursive Ensemble Approach for Incremental Learning of Non-Stationary Imbalanced Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 17
%P 41-45
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Learning non-stationary data stream is much difficult as many real world data mining applications involve learning from imbalanced data sets. Imbalance dataset consist of data having minority and majority classes. Classifiers have high productivity accuracy on majority classes and Low productivity accuracy on minority classes. Imbalanced class partition over data stream demands a technique to intensify the underrepresented class concepts for increased overall performance. To alleviate the challenges brought by these problems, this paper propose the recursive ensemble approach (REA). This approach reduces problem of imbalance data by learning minority and majority instances arrived at incremental time. In Practical analysis REA results are compare with Synthetic Minority Over-sampling Technique (SMOTE) and predicted results proves that REA gives better performance as compare to SMOTE on synthetic and real time datasets.

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Index Terms

Computer Science
Information Sciences

Keywords

Class Imbalance Incremental Learning Non-Stationary REA SMOTE.