Comparing the dimensionality reduction methods in gene expression databases

Dimensionality reduction has been applied in the most different areas, among which the data analysis of gene expression obtained with the microarray approach. The data involved in this problem is challenging for machine learning algorithms due to a small number of samples and a high number of attributes. This paper proposes a preprocessing phase by means of attribute selection and random projection method in microarray data. Experimental results are promising and show that the use of these methods improves the performance of classification algorithms.

► Data used is challenging, since there a few examples and a huge set of attributes. ► A comparison among attribute selection methods and random projection is made. ► Detailed comparisons are presented. ► Statistical significance of the methods show the results.