Paper Info
Title
A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection
Abstract
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.
Year
DOI
Venue
2015
10.1080/00207721.2013.801096
Int. J. Systems Science
Keywords
Field
DocType
mrmr,medical diagnosis,extreme learning machine,feature selection,erythemato-squamous diseases diagnosis
Dimensionality reduction,Pattern recognition,Feature selection,Extreme learning machine,Computer science,Redundancy (engineering),Minimum redundancy feature selection,Artificial intelligence,Classifier (linguistics),Machine learning,Medical diagnosis
Journal
Volume
Issue
ISSN
46
5
0020-7721
Citations 
PageRank 
References 
12
0.53
34
Authors
5
Name
Order
Citations
PageRank
Tong Liu1120.53
Liang Hu2120.53
Chao Ma3341.55
Zhi-Yan Wang4120.53
Hui-Ling Chen5171.00