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Microarray classification with hierarchical data representation and novel feature selection criteria

Autor
Bosio, M.; Bellot, P.; Salembier, P.; Oliveras, A.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
12th IEEE International Conference on BioInformatics and BioEngineering
Any de l'edició
2012
Data de presentació
2012-11-12
Llibre d'actes
Proceedings of the 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE) : Larnaca, Cyprus, 11-13 November 2012
Pàgina inicial
344
Pàgina final
349
Editor
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/BIBE.2012.6399648 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/18291 Obrir en finestra nova
URL
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6399648&tag=1 Obrir en finestra nova
Resum
Microarray data classification is a challenging prob- lem due to the high number of variables compared to the small number of available samples. An effective methodology to output a precise and reliable classifier is proposed in this work as an improvement of the algorithm in [1]. It considers the sample scarcity problem and the lack of data structure typical of microarrays. Both problem are assessed by a two-step approach applying hierarchical clustering to create new features called metagenes ...
Citació
Bosio, M. [et al.]. Microarray classification with hierarchical data representation and novel feature selection criteria. A: IEEE International Conference on BioInformatics and BioEngineering. "Proceedings of the 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE) : Larnaca, Cyprus, 11-13 November 2012". Larnaca: Institute of Electrical and Electronics Engineers (IEEE), 2012, p. 344-349.
Paraules clau
LDA, Microarray classification, Treelets, feature selection, hierarchical representation, metagenes, wrapper
Grup de recerca
GPI - Grup de Processament d'Imatge i Vídeo
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants