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Multiclass cancer-microarray classification algorithm with Pair-Against-All redundancy

Author
Bosio, M.; Bellot, P.; Salembier, P.; Oliveras, A.
Type of activity
Presentation of work at congresses
Name of edition
2012 IEEE International Workshop on Genomic Signal Processing and Statistic
Date of publication
2012
Presentation's date
2012-12-03
Book of congress proceedings
Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistic
First page
1
Last page
4
DOI
https://doi.org/10.1109/GENSIPS.2012.6507741 Open in new window
Repository
http://hdl.handle.net/2117/87164 Open in new window
Abstract
Multiclass cancer classification is still a challenging task in the field of machine learning. A novel multiclass approach is proposed in this work as a combination of multiple binary classifiers. It is an example of Error Correcting Output Codes algorithms, applying data transmission coding techniques to improve the classification as a combination of binary classifiers. The proposed method combines the One Against All, OAA, approach with a set of classifiers separating each class-pair from the ...
Citation
Bosio, M., Bellot, P., Salembier, P., Oliveras, A. Multiclass cancer-microarray classification algorithm with Pair-Against-All redundancy. A: IEEE International Workshop on Genomic Signal Processing and Statistic. "Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistic". Washington: 2012, p. 1-4.
Keywords
Classification, ECOC, Microarray, Multiclass
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants