Abstract
DNA microarray allows the measurement of expression levels of tens of thousands of genes simultaneously and has many applications in biology and medicine. Microarray data are very noisy and this makes it difficult for data analysis and classification. Sub-dimension based methods can overcome the noise problem by partitioning the conditions into sub-groups, performing classification with each group and integrating the results. However, there can be many sub-dimensional groups, which lead to a high computational complexity. In this paper, we propose an entropy-based method to evaluate and select important sub-dimensions and eliminate unimportant ones. This improves the computational efficiency considerably. We have tested our method on four microarray datasets and two other real-world datasets and the experiment results prove the effectiveness of our method.