Large scale feature selection using modified random mutation hill climbing

Feature selection is a critical component of many pattern recognition applications. There are two distinct mechanisms for feature selection, namely the wrapper methods and the filter methods. The filter methods are generally considered inferior to the wrapper method, however wrapper methods are computationally more demanding than filter methods. One of the popular methods for wrapper-based feature selection is random mutation hill climbing. It performs a random search over the feature space to derive the optimal set of features. We would describe two enhancements to this algorithm, one that would improve its convergence time, and the other that would allow us to bias the results towards either higher accuracy or lower final feature space dimensionality. We would apply the algorithm to a real-world massive-scale feature selection problem involving the image classification problem associated with suppressing automobile airbags for children. We would provide classification results on an image database of nearly 4,000 images that indicate the advantages of the proposed method.