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Reseach Article

A Review on Imbalanced Learning Methods

Published on December 2015 by Varsha S. Babar, Roshani Ade
National Conference on Advances in Computing
Foundation of Computer Science USA
NCAC2015 - Number 2
December 2015
Authors: Varsha S. Babar, Roshani Ade

Varsha S. Babar, Roshani Ade . A Review on Imbalanced Learning Methods. National Conference on Advances in Computing. NCAC2015, 2 (December 2015), 23-27.

author = { Varsha S. Babar, Roshani Ade },
title = { A Review on Imbalanced Learning Methods },
journal = { National Conference on Advances in Computing },
issue_date = { December 2015 },
volume = { NCAC2015 },
number = { 2 },
month = { December },
year = { 2015 },
issn = 0975-8887,
pages = { 23-27 },
numpages = 5,
url = { /proceedings/ncac2015/number2/23366-5029/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 National Conference on Advances in Computing
%A Varsha S. Babar
%A Roshani Ade
%T A Review on Imbalanced Learning Methods
%J National Conference on Advances in Computing
%@ 0975-8887
%V NCAC2015
%N 2
%P 23-27
%D 2015
%I International Journal of Computer Applications

Nowadays learning from imbalanced data sets are a relatively a very critical task for many data mining applications such as fraud detection, anomaly detection, medical diagnosis, information retrieval systems. The imbalanced learning problem is nothing but unequal distribution of data between the classes where one class contains more and more samples while another contains very little. Because of imbalance learning problems, it becomes hard for the classifier to learn the minority class samples. The Aim of this paper is to review on various techniques which are used for resolving imbalanced learning problem. This paper proposes a taxonomy for various methods used forhandling the class imbalance problem where each method can be categorized depending on the techniques it uses. To handle imbalanced learning problem significant work has been done, which can be categorized into four categories: sampling-based methods, cost-based methods, kernel-based methods, and active learning-based methods. All these methods resolve the imbalanced learning problem efficiently.

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Index Terms

Computer Science
Information Sciences


Imbalanced Learning Active Learning Cost-sensitive Learning