Active Learning with Wavelets for Microarray Data

Abstract

In Supervised Learning it is assumed that is straightforward to obtained labeled data. However, in reality labeled data can be scarce or expensive to obtain. Active Learning (AL) is a way to deal with the above problem by asking for the labels of the most “informative” data points. We propose a novel AL method based on wavelet analysis, which pertains especially to the large number of dimensions (i.e. examined genes) of microarray experiments. DNA Microarray expression experiments permit the systematic study of the correlation of the expression of thousands of genes. We have applied our method on such data sets with encouraging results. In particular we studied data sets concerning: Small Round Blue Cell Tumours (4 types), Leukemia (2 types) and Lung Cancer (2 types).