Applying Particle Swarm Intelligence for Feature Selection of Spectral Imagery

Feature selection is necessary to reduce the dimensionality of spectral image data. Particle swarm optimization was originally developed to search only continuous spaces and, although many applications on discrete spaces had been proposed, it could not tackle the problem of feature selection directly. We developed a formulation utilizing two particles swarms in order to optimize a desired performance criterion and the number of selected features, simultaneously. Candidate feature sets were evaluated on a regression problem modeled using neural networks, which were trained to construct models of chemical concentration of glucose in soybeans. We present experimental results utilizing real-world spectral image data to attest the viability of the method. The particle swarms approach presented superior performance for linear modeling of chemical contents when compared to a conventional feature extraction method.