Creating and sharing knowledge for telecommunications

Feature Discretization and Selection in Microarray Data

Ferreira, A. ; Figueiredo, M. A. T.

Feature Discretization and Selection in Microarray Data, Proc International Conf. on Knowledge Discovery and Information Retrieval - KDIR, Paris, France, Vol. 1, pp. 465 - 469, October, 2011.

Digital Object Identifier:

Download Full text PDF ( 81 KBs)

Abstract
Tumor and cancer detection from microarray data are important bioinformatics problems. These problems are quite challenging for machine learning methods, since microarray datasets typically have a very large number of features and small number of instances. Learning algorithms are thus confronted with the curse of
dimensionality, and need to address it in order to be effective. This paper proposes unsupervised feature discretization
and selection methods suited for microarray data. The experimental results reported, conducted on public domain microarray datasets, show that the proposed discretization and selection techniques yield competitive and promising results with the best previous approaches. Moreover, the proposed methods efficiently handle multi-class microarray data.