dc.contributor.author | Catak, Ferhat Özgur | |
dc.date.accessioned | 2020-03-20T13:37:30Z | |
dc.date.available | 2020-03-20T13:37:30Z | |
dc.date.created | 2020-01-17T11:26:18Z | |
dc.date.issued | 2015 | |
dc.identifier.isbn | 978-1-4673-7386-9 | |
dc.identifier.uri | https://hdl.handle.net/11250/2647879 | |
dc.description.abstract | In this age of Big Data, machine learning based data mining methods are extensively used to inspect large scale data sets. Deriving applicable predictive modeling from these type of data sets is a challenging obstacle because of their high complexity. Opportunity with high data availability levels, automated classification of data sets has become a critical and complicated function. In this paper, the power of applying MapReduce based Distributed AdaBoosting of Extreme Learning Machine (ELM) are explored to build reliable predictive bag of classification models. Thus, (i) dataset ensembles are build; (ii) ELM algorithm is used to build weak classification models; and (iii) build a strong classification model from a set of weak classification models. This training model is applied to the publicly available knowledge discovery and data mining datasets. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.title | Classification with Extreme Learning Machine and ensemble algorithms over randomly partitioned data | en_US |
dc.type | Chapter | en_US |
dc.description.version | acceptedVersion | en_US |
dc.source.pagenumber | 4 | en_US |
dc.identifier.doi | 10.1109/SIU.2015.7129801 | |
dc.identifier.cristin | 1775628 | |
dc.description.localcode | © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
cristin.unitcode | 194,63,30,0 | |
cristin.unitname | Institutt for informasjonssikkerhet og kommunikasjonsteknologi | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |