Supervised classification and gene selection using simulated annealing

Maurizio Filippone*, Francesco Masulli, Stefano Rovetta

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations


Genomic data are often characterized by small cardinality and high dimensionality. For those data, a feature selection procedure could highlight the relevant genes and improve the classification results. In this paper we propose a wrapper approach to gene selection in Classification of gene expression data using Simulated Annealing and SVM. The proposed approach can do global combinatorial searches through the space of possible input subsets, can handle cases with numerical, categorical or mixed inputs, and is able to find (sub-)optimal subsets of input variables giving very low classification errors. The method has been tested on the publicly available data sets Leukemia by Golub et al. and Colon by Alon at al. The experimental results highlight the capacity of the method to select minimal sets of relevant genes.

Original languageEnglish (US)
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)0780394909, 9780780394902
StatePublished - 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: Jul 16 2006Jul 21 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576


ConferenceInternational Joint Conference on Neural Networks 2006, IJCNN '06
CityVancouver, BC

ASJC Scopus subject areas

  • Software


Dive into the research topics of 'Supervised classification and gene selection using simulated annealing'. Together they form a unique fingerprint.

Cite this