Optimized pruned annular extreme learning machines

Lavneet Singh, Girija Chetty

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review


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
Number of pages12
ISBN (Electronic)9781921770173
Publication statusPublished - 1 Jan 2014
EventTwelfth Australasian Data Mining Conference - Brisbane, Brisbane, Australia
Duration: 27 Nov 201428 Nov 2014


ConferenceTwelfth Australasian Data Mining Conference
Abbreviated titleAusDM14
Internet address


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