The painter's feature selection for gene expression data

Daniele Apiletti, Elena Baralis, Giulia Bruno, Alessandro Fiori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Feature selection is a fundamental task in microarray data analysis. It aims at identifying the genes which are mostly associated with a tissue category, disease state or clinical outcome. An effective feature selection reduces computation costs and increases classification accuracy. This paper presents a novel multi-class approach to feature selection for gene expression data, which is called Painter's approach. It has the benefits of both a parameter free technique and a native multicategory method. It consists of two phases. The first is a filtering phase that smooths the effect of noise and outliers, which represent a common problem in microarray data. In the second phase, the actual gene selection is performed. Preliminary experimental results on three public datasets are presented. They confirm the intuition of the proposed approach leading to high classification accuracies.

Original languageEnglish
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Pages4227-4230
Number of pages4
DOIs
Publication statusPublished - 2007
Event29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07 - Lyon, France
Duration: Aug 23 2007Aug 26 2007

Other

Other29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
CountryFrance
CityLyon
Period8/23/078/26/07

ASJC Scopus subject areas

  • Biomedical Engineering

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