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 Survey on Solution of Imbalanced Data Classification Problem Using SMOTE and Extreme Learning Machine Subject The topic of the resource Extreme learning machine (ELM); Imbalanced data (ID); Imbalanced data classification; Machine learning; Machine learning (ML); Synthetic minority over-sampling technique (SMOTE) Description An account of the resource Imbalanced data are a common classification problem. Since it occurs in most real fields, this trend is increasingly important. It is of particular concern for highly imbalanced datasets (when the class ratio is high). Different techniques have been developed to deal with supervised learning sets. SMOTE is a well-known method for over-sampling that discusses imbalances at the level of the data. In the area, unequal data are widely distributed, and ensemble learning algorithms are a more efficient classifier in classifying imbalances. SMOTE synthetically contrasts two closely connected vectors. The learning algorithm itself, however, is not designed for imbalanced results. The simple ensemble idea, as well as the SMOTE algorithm, works with imbalanced data. There are detailed studies about imbalanced data problems and resolving this problem through several approaches. There are various approaches to overcome this problem, but we mainly focused on SMOTE and extreme learning machine algorithms. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Goyal A.; Rathore L.; Kumar S. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-204, pp. 31-44. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-16-1089-9_4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-1089-9_4</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112006004&amp;doi=10.1007%2F978-981-16-1089-9_4&amp;partnerID=40&amp;md5=5501083f7e0ea5d46bd59e1096cdfafe" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112006004&amp;doi=10.1007%2f978-981-16-1089-9_4&amp;partnerID=40&amp;md5=5501083f7e0ea5d46bd59e1096cdfafe</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981161088-2 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 Goyal A., KL (Deemed to be University), Hyderabad, 500075, India; Rathore L., Yagyavalkya Institute of Technology, Jaipur, India; Kumar S., CHRIST (Deemed to be University), Bangalore, Karnataka, 560074, India