Microarray classification with hierarchical data representation and novel feature selection criteria
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
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 . 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 and introducing a novel feature ranking criterion, inside the wrapper feature selection task. The classification ability has been evaluated on 4 publicly available datasets from Micro Array Quality Control study phase II (MAQC) classified by 7 different endpoints. The global results have showed how the proposed approach obtains better prediction accuracy than a wide variety of state of the art alternatives
CitationBosio, 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.
|cBosio12.pdf||Article principal||513,5Kb||Restricted access|