A review on ensembles-based approach to overcome class imbalance problem

Title

A review on ensembles-based approach to overcome class imbalance problem

Creator

Kumar S.; Madhuri J.N.; Goswami M.

Description

Predictive analytics incorporate various statistical techniques from predictive modelling, machine learning and data mining to analyse large database for future prediction. Data mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. With the improvement in technology day by day large amount of data are collected in raw form and as a result necessity of using data mining techniques in various domains are increasing. Class imbalance is an open challenge problem in data mining and machine learning. It occurs due to imbalanced data set. A data set is considered as imbalanced when a data set contains number of instance in one class vastly outnumber the number of instances in other class. When traditional data mining algorithms trained with imbalanced data sets, it gives suboptimal classification model. Recently class imbalance problem have gain significance attention from data mining and machine learning researcher community due to its presence in many real world problem such as remote-sensing, pollution detection, risk management, fraud detection and medical diagnosis. Several methods have been proposed to overcome the problem of class imbalance problem. In this paper, our goal is to review various methods which are proposed to overcome the effect of imbalance data on classification learning algorithms. Springer Nature Singapore Pte Ltd 2019.

Source

Advances in Intelligent Systems and Computing, Vol-906, pp. 155-172.

Date

2019-01-01

Publisher

Springer Verlag

Subject

Bagging; Boosting; Class imbalance; Classification; Ensemble; Ensemble approach for class imbalance; Sampling

Coverage

Kumar S., Department of CSE, Christ(Deemed to BeUniversity), Bengaluru, 560029, India; Madhuri J.N., Department of CSE, Christ(Deemed to BeUniversity), Bengaluru, 560029, India; Goswami M., Department of CSE, Christ(Deemed to BeUniversity), Bengaluru, 560029, India

Rights

Restricted Access

Relation

ISSN: 21945357; ISBN: 978-981136000-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-13-6001-5_12

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072899477&doi=10.1007%2f978-981-13-6001-5_12&partnerID=40&md5=2a4f178181954f3c0e87d34fced7d106