Glowworm Swarm Based Informative Attribute Selection Using Support Vector Machines for Simultaneous Feature Selection and Classification

In this paper, we propose a hybrid filter-wrapper algorithm, GSO-Infogain, for simultaneous feature selection for improved classification accuracy. GSO-Infogain employs Glowworm-Swarm Optimization(GSO) algorithm with Support Vector Machine(SVM) as its internal learning algorithm and utilizes feature ranking based on information gain as a heuristic. The GSO algorithm randomly generates a population of worms, each of which is a candidate subset of features. The fitness of each candidate solution, which is evaluated using Support Vector Machine, is encoded within its luciferin value. Each worm probabilistically moves towards the worm with the highest luciferin value in its neighbourhood. In the process, they explore the feature space and eventually converge to the global optimum. We have evaluated the performance of the hybrid algorithm for feature selection on a set of cancer datasets. We obtain a classification accuracy in the range 94-98% for these datasets, which is comparable to the best results from other classification algorithms. We further tested the robustness of GSO-Infogain by evaluating its performance on the CoEPrA training and test datasets. GSO-Infogain performs well in this experiment too by giving similar prediction accuracies on the training and test datasets thus indicating its robustness.