Learning ELM-Tree from big data based on uncertainty reduction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)79-100
Journal / PublicationFuzzy Sets and Systems
Volume258
Online published14 May 2014
Publication statusPublished - 1 Jan 2015

Abstract

A challenge in big data classification is the design of highly parallelized learning algorithms. One solution to this problem is applying parallel computation to different components of a learning model. In this paper, we first propose an extreme learning machine tree (ELM-Tree) model based on the heuristics of uncertainty reduction. In the ELM-Tree model, information entropy and ambiguity are used as the uncertainty measures for splitting decision tree (DT) nodes. Besides, in order to resolve the over-partitioning problem in the DT induction, ELMs are embedded as the leaf nodes when the gain ratios of all the available splits are smaller than a given threshold. Then, we apply parallel computation to five components of the ELM-Tree model, which effectively reduces the computational time for big data classification. Experimental studies demonstrate the effectiveness of the proposed method.

Research Area(s)

  • Big data classification, Decision tree, ELM-Tree, Extreme learning machine, Uncertainty reduction

Citation Format(s)

Learning ELM-Tree from big data based on uncertainty reduction. / Wang, Ran; He, Yu-Lin; Chow, Chi-Yin; Ou, Fang-Fang; Zhang, Jian.

In: Fuzzy Sets and Systems, Vol. 258, 01.01.2015, p. 79-100.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review