4.7 Article

A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection

期刊

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
卷 46, 期 5, 页码 919-931

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2013.801096

关键词

extreme learning machine; erythemato-squamous diseases diagnosis; feature selection; medical diagnosis; MRMR

资金

  1. National High Technology Research and Development Program of China [2011AA010101]
  2. National Natural Science Foundation of China [61103197, 61073009, 61202171, 61272018]
  3. China Postdoctoral Science Foundation [2012M521251]
  4. Key Programs for Science and Technology Development of Jilin Province of China [2011ZDGG007]
  5. Youth Foundation of Jilin Province of China [201101035]
  6. Zhejiang Provincial Natural Science Foundation of China [R1110261, Y1100769]
  7. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University [93K172013K01]

向作者/读者索取更多资源

In this paper, a novel hybrid method, which integrates an effective filter maximum relevance minimum redundancy (MRMR) and a fast classifier extreme learning machine (ELM), has been introduced for diagnosing erythemato-squamous (ES) diseases. In the proposed method, MRMR is employed as a feature selection tool for dimensionality reduction in order to further improve the diagnostic accuracy of the ELM classifier. The impact of the type of activation functions, the number of hidden neurons and the size of the feature subsets on the performance of ELM have been investigated in detail. The effectiveness of the proposed method has been rigorously evaluated against the ES disease dataset, a benchmark dataset, from UCI machine learning database in terms of classification accuracy. Experimental results have demonstrated that our method has achieved the best classification accuracy of 98.89% and an average accuracy of 98.55% via 10-fold cross-validation technique. The proposed method might serve as a new candidate of powerful methods for diagnosing ES diseases.

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