Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction | Zendy

Nathalie Pochet | Zendy; Frank De Smet | Zendy; Johan A. K. Suykens | Zendy; Bart L. R. De Moor | Zendy

Open Access

Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction

Author(s) -

Nathalie Pochet,

Frank De Smet,

Johan A. K. Suykens,

Bart L. R. De Moor

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bth383

Subject(s) - overfitting , dimensionality reduction , artificial intelligence , support vector machine , kernel (algebra) , pattern recognition (psychology) , benchmarking , radial basis function kernel , computer science , kernel method , principal component analysis , linear classifier , kernel principal component analysis , polynomial kernel , feature selection , mathematics , artificial neural network , combinatorics , marketing , business

Microarrays are capable of determining the expression levels of thousands of genes simultaneously. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. The aim of this paper is to systematically benchmark the role of non-linear versus linear techniques and dimensionality reduction methods.

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