  Microarray cancer gene expression data comprise of very high dimensions.
Reducing the dimensions helps in improving the overall analysis and
classification performance. We propose two hybrid techniques, Biogeography -
based Optimization - Random Forests (BBO - RF) and BBO - SVM (Support Vector
Machines) with gene ranking as a heuristic, for microarray gene expression
analysis. This heuristic is obtained from information gain filter ranking
procedure. The BBO algorithm generates a population of candidate subset of
genes, as part of an ecosystem of habitats, and employs the migration and
mutation processes across multiple generations of the population to improve the
classification accuracy. The fitness of each gene subset is assessed by the
classifiers - SVM and Random Forests. The performances of these hybrid
techniques are evaluated on three cancer gene expression datasets retrieved
from the Kent Ridge Biomedical datasets collection and the libSVM data
repository. Our results demonstrate that genes selected by the proposed
techniques yield classification accuracies comparable to previously reported
algorithms.
