Class-specific soft voting based multiple extreme learning machines ensemble

Jingjing CAO, Sam KWONG, Ran WANG, Xiaodong LI, Ke LI, Xiangfei KONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

47 Citations (Scopus)


Compared with conventional weighted voting methods, class-specific soft voting (CSSV) system has several advantages. On one hand, it not only deals with the soft class probability outputs but also refines the weights from classifiers to classes. On the other hand, the class-specific weights can be used to improve the combinative performance without increasing much computational load. This paper proposes two weight optimization based ensemble methods (CSSV-ELM and SpaCSSV-ELM) under the framework of CSSV scheme for multiple extreme learning machines (ELMs). The designed two models are in terms of accuracy and sparsity aspects, respectively. Firstly, CSSV-ELM takes advantage of the condition number of matrix, which reveals the stability of linear equation, to determine the weights of base ELM classifiers. This model can reduce the unreliability induced by randomly input parameters of a single ELM, and solve the ill-conditioned problem caused by linear system structure of ELM simultaneously. Secondly, sparse ensemble methods can lower memory requirement and speed up the classification process, but only for classifier-specific weight level. Therefore, a SpaCSSV-ELM method is proposed by transforming the weight optimization problem to a sparse coding problem, which uses the sparse representation technique for maintaining classification performance with less nonzero weight coefficients. Experiments are carried out on twenty UCI data sets and Finance event series data and the experimental results show the superior performance of the CSSV based ELM algorithms by comparing with the state-of-the-art algorithms.
Original languageEnglish
Pages (from-to)275-284
Issue numberPart A
Publication statusPublished - 3 Feb 2015
Externally publishedYes

Bibliographical note

The work described in this paper was partially supported by the Fundamental Research Funds for the Central Universities ( WUT:2014-IV-054 ), National Natural Science Foundation of China under the Grant No. 61175123, City University Applied Research Grant 9667094 and Chinese National Science Foundation (Grant No. 61272289 ).


  • Condition number
  • Extreme learning machine
  • Soft voting
  • Sparse ensemble


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