| Multi-category classification using an Extreme Learning Machine for microarray gene expression cancer diagnosis. | |
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MedLine Citation:
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PMID: 17666768 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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In this paper, the recently developed Extreme Learning Machine (ELM) is used for direct multicategory classification problems in the cancer diagnosis area. ELM avoids problems like local minima, improper learning rate and overfitting commonly faced by iterative learning methods and completes the training very fast. We have evaluated the multi-category classification performance of ELM on three benchmark microarray datasets for cancer diagnosis, namely, the GCM dataset, the Lung dataset and the Lymphoma dataset. The results indicate that ELM produces comparable or better classification accuracies with reduced training time and implementation complexity compared to artificial neural networks methods like conventional back-propagation ANN, Linder's SANN, and Support Vector Machine methods like SVM-OVO and Ramaswamy's SVM-OVA. ELM also achieves better accuracies for classification of individual categories. |
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Authors:
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Runxuan Zhang; Guang-Bin Huang; Narasimhan Sundararajan; P Saratchandran |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM Volume: 4 ISSN: 1545-5963 ISO Abbreviation: IEEE/ACM Trans Comput Biol Bioinform Publication Date: 2007 Jul-Sep |
Date Detail:
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Created Date: 2007-08-01 Completed Date: 2007-10-19 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101196755 Medline TA: IEEE/ACM Trans Comput Biol Bioinform Country: United States |
Other Details:
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Languages: eng Pagination: 485-95 Citation Subset: IM |
Affiliation:
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Systems Biology Unit, Department of Genomes and Genetics, Institut Pasteur, France. rzhang@pasteur.fr |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Artificial Intelligence* Diagnosis, Computer-Assisted / methods Gene Expression Profiling / methods* Humans Neoplasm Proteins / metabolism* Neoplasms / diagnosis, metabolism* Oligonucleotide Array Sequence Analysis / methods* Pattern Recognition, Automated / methods* Tumor Markers, Biological / metabolism* |
| Chemical | |
Reg. No./Substance:
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0/Neoplasm Proteins; 0/Tumor Markers, Biological |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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