A Filter-Based Evolutionary Approach for Selecting Features in High-Dimensional Micro-array Data - Intelligent Information Processing V Access content directly
Conference Papers Year : 2010

A Filter-Based Evolutionary Approach for Selecting Features in High-Dimensional Micro-array Data

Abstract

Evolutionary algorithms have received much attention in extracting knowledge on high-dimensional micro-array data, being crucial to their success a suitable definition of the search space of the potential solutions. In this paper, we present an evolutionary approach for selecting informative genes (features) to predict and diagnose cancer. We propose a procedure that combines results of filter methods, which are commonly used in the field of data mining, to reduce the search space where a genetic algorithm looks for solutions (i.e. gene subsets) with better classification performance, being the quality (fitness) of each solution evaluated by a classification method. The methodology is quite general because any classification algorithm could be incorporated as well a variety of filter methods. Extensive experiments on a public micro-array dataset are presented using four popular filter methods and SVM.
Fichier principal
Vignette du fichier
3400307.pdf (398.64 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01060366 , version 1 (04-09-2014)

Licence

Attribution

Identifiers

Cite

Laura Maria Cannas, Nicoletta Dessì, Barbara Pes. A Filter-Based Evolutionary Approach for Selecting Features in High-Dimensional Micro-array Data. 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP), Oct 2010, Manchester, United Kingdom. pp.297-307, ⟨10.1007/978-3-642-16327-2_36⟩. ⟨hal-01060366⟩
84 View
136 Download

Altmetric

Share

Gmail Facebook X LinkedIn More