title: The painter's feature selection for gene expression data.
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.
authors.affiliation: Politecnico di Torino, Italy. daniele.apiletti@polito.it
authors.forename: Daniele
authors.initials: D
authors.lastname: Apiletti
authors.forename: Elena
authors.initials: E
authors.lastname: Baralis
authors.forename: Giulia
authors.initials: G
authors.lastname: Bruno
authors.forename: Alessandro
authors.initials: A
authors.lastname: Fiori
mesh_terms: D000818:Animals; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011237:Predictive Value of Tests; D012984:Software