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Reducing multiclass cancer classification to binary by output coding and SVM

Paper ID Volume ID Publish Year Pages File Format Full-Text
15416 1411 2006 9 PDF Available
Title
Reducing multiclass cancer classification to binary by output coding and SVM
Abstract

Multiclass cancer classification based on microarray data is presented. The binary classifiers used combine support vector machines with a generalized output-coding scheme. Different coding strategies, decoding functions and feature selection methods are incorporated and validated on two cancer datasets: GCM and ALL. Using random coding strategy and recursive feature elimination, the testing accuracy achieved is as high as 83% on GCM data with 14 classes. Comparing with other classification methods, our method is superior in classificatory performance.

Keywords
Multiclass; Cancer classification; Microarrays; Output coding; Support vector machine
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Reducing multiclass cancer classification to binary by output coding and SVM
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 30, Issue 1, February 2006, Pages 63–71
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us