Modified Binary Inertial Particle Swarm Optimization for Gene Selection in DNA Microarray Data

DNA microarrays are being used to characterize the genetic expression of several illnesses, such as cancer. There has been interest in developing automated methods to classify the data generated by those microarrays. The problem is complex due to the availability of just a few samples to train the classifiers, and the fact that each sample may contain several thousands of features. One possibility is to select a reduced set of features genes. In this work we propose a wrapper method that is a modified version of the Inertial Geometric Particle Swarm Optimization.We name it MIGPSO. We compare MIGPSO with other approaches. The results are promising. MIGPSO obtained an increase in accuracy of about 4i¾?%. The number of genes selected is also competitive.