Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A review on ensembles-based approach to overcome class imbalance problem Subject The topic of the resource Bagging; Boosting; Class imbalance; Classification; Ensemble; Ensemble approach for class imbalance; Sampling Description An account of the resource 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. Creator An entity primarily responsible for making the resource Kumar S.; Madhuri J.N.; Goswami M. Source A related resource from which the described resource is derived Advances in Intelligent Systems and Computing, Vol-906, pp. 155-172. Publisher An entity responsible for making the resource available Springer Verlag Date A point or period of time associated with an event in the lifecycle of the resource 2019-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-13-6001-5_12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-13-6001-5_12</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072899477&amp;doi=10.1007%2F978-981-13-6001-5_12&amp;partnerID=40&amp;md5=2a4f178181954f3c0e87d34fced7d106" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072899477&amp;doi=10.1007%2f978-981-13-6001-5_12&amp;partnerID=40&amp;md5=2a4f178181954f3c0e87d34fced7d106</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21945357; ISBN: 978-981136000-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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