title: Stable feature selection and classification algorithms for multiclass microarray data.
abstract: BACKGROUND
  Recent studies suggest that gene expression profiles are a promising alternative for clinical cancer classification. One major problem in applying DNA microarrays for classification is the dimension of obtained data sets. In this paper we propose a multiclass gene selection method based on Partial Least Squares (PLS) for selecting genes for classification. The new idea is to solve multiclass selection problem with the PLS method and decomposition to a set of two-class sub-problems: one versus rest (OvR) and one versus one (OvO). We use OvR and OvO two-class decomposition for other recently published gene selection method. Ranked gene lists are highly unstable in the sense that a small change of the data set often leads to big changes in the obtained ordered lists. In this paper, we take a look at the assessment of stability of the proposed methods. We use the linear support vector machines (SVM) technique in different variants: one versus one, one versus rest, multiclass SVM (MSVM) and the linear discriminant analysis (LDA) as a classifier. We use balanced bootstrap to estimate the prediction error and to test the variability of the obtained ordered lists.
  
  
  RESULTS
  This paper focuses on effective identification of informative genes. As a result, a new strategy to find a small subset of significant genes is designed. Our results on real multiclass cancer data show that our method has a very high accuracy rate for different combinations of classification methods, giving concurrently very stable feature rankings.
  
  
  CONCLUSIONS
  This paper shows that the proposed strategies can improve the performance of selected gene sets substantially. OvR and OvO techniques applied to existing gene selection methods improve results as well. The presented method allows to obtain a more reliable classifier with less classifier error. In the same time the method generates more stable ordered feature lists in comparison with existing methods.
authors.affiliation: Institute of Automatic Control, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland. sebastian.student@polsl.pl
authors.forename: Sebastian
authors.initials: S
authors.lastname: Student
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Fujarewicz
mesh_terms: D016002:Discriminant Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D016018:Least-Squares Analysis; D016014:Linear Models; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D060388:Support Vector Machine; D059467:Transcriptome