Optimized pruned annular extreme learning machines

Lavneet Singh, Girija Chetty

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

Data mining with big datasets and large samples can be problematic, due to increase in complexity and computational times, and bad generalization due to outliers. Using the motivation from extreme learning machines (ELM), in this paper, we propose a novel approach based on annular ELM, involving RANSAC multi model response regularization. Experimental results on different benchmark datasets showed that proposed algorithm based on annular ELM can optimally prune the hidden nodes, and allow better generalization and higher classification accuracy to be achieved as compared to other algorithms, including SVM and OP-ELM for binary and multi-class classification and regression problems.

Original languageEnglish
Title of host publicationConferences in Research and Practice in Information Technology Series
Subtitle of host publicationData Mining and Analytics 2014 - Proceedings of the 12th Australasian Data Mining Conference, AusDM 2014
PublisherAustralian Computer Society
Pages101-112
Number of pages12
Volume158
ISBN (Electronic)9781921770173
Publication statusPublished - 1 Jan 2014
EventTwelfth Australasian Data Mining Conference - Brisbane, Brisbane, Australia
Duration: 27 Nov 201428 Nov 2014
http://ausdm14.ausdm.org/

Conference

ConferenceTwelfth Australasian Data Mining Conference
Abbreviated titleAusDM14
CountryAustralia
CityBrisbane
Period27/11/1428/11/14
Internet address

Fingerprint

Learning systems
Data mining

Cite this

Singh, L., & Chetty, G. (2014). Optimized pruned annular extreme learning machines. In Conferences in Research and Practice in Information Technology Series: Data Mining and Analytics 2014 - Proceedings of the 12th Australasian Data Mining Conference, AusDM 2014 (Vol. 158, pp. 101-112). Australian Computer Society.
Singh, Lavneet ; Chetty, Girija. / Optimized pruned annular extreme learning machines. Conferences in Research and Practice in Information Technology Series: Data Mining and Analytics 2014 - Proceedings of the 12th Australasian Data Mining Conference, AusDM 2014. Vol. 158 Australian Computer Society, 2014. pp. 101-112
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Singh, L & Chetty, G 2014, Optimized pruned annular extreme learning machines. in Conferences in Research and Practice in Information Technology Series: Data Mining and Analytics 2014 - Proceedings of the 12th Australasian Data Mining Conference, AusDM 2014. vol. 158, Australian Computer Society, pp. 101-112, Twelfth Australasian Data Mining Conference, Brisbane, Australia, 27/11/14.

Optimized pruned annular extreme learning machines. / Singh, Lavneet; Chetty, Girija.

Conferences in Research and Practice in Information Technology Series: Data Mining and Analytics 2014 - Proceedings of the 12th Australasian Data Mining Conference, AusDM 2014. Vol. 158 Australian Computer Society, 2014. p. 101-112.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Singh L, Chetty G. Optimized pruned annular extreme learning machines. In Conferences in Research and Practice in Information Technology Series: Data Mining and Analytics 2014 - Proceedings of the 12th Australasian Data Mining Conference, AusDM 2014. Vol. 158. Australian Computer Society. 2014. p. 101-112