Hybridizing Extreme Learning Machines and Genetic Algorithms to select acoustic features in vehicle classification applications

Currently traffic noise has become an important factor that affects human health, and thus, an application able to classify vehicles on the basis of the sound they produce becomes important in the effort of fulfilling recommendations that aim at reducing traffic noise and improving intelligent transportation systems. This paper focuses on the problem of selecting those sound-describing features that make the vehicle classifier work properly. In particular, the goal of this paper is to evaluate the feasibility of a novel feature selection method based on a special class of Genetic Algorithm (with restricted search) hybridized with a Extreme Learning Machine. Because of its great generalization performance at a very fast learning speed, the Extreme Learning Machine plays the key role of providing the fitness of candidate solutions in each generation of the Genetic Algorithm. After a number of experiments comparing its performance to that of other fast learning algorithms, our approach has been found to be the most feasible for the application at hand. The proposed method helps the Extreme Learning Machine-based classifier to increase its performance from a mean probability of correct classification of 74.83% (with no feature selection) up to 93.74% (when using the optimum subset of selected features).