Abstract:
We present a new feature selection algorithm for structure-activity and structure-property correlation based on particle swarms. Particle swarms explore the search space through a population of individuals, which adapt by returning stochastically towards previously successful regions, influenced by the success of their neighbors. This method, which was originally intended for searching multidimensional continuous spaces, is adapted to the problem of feature selection by viewing the location vectors of the particles as probabilities, and employing roulette wheel selection to construct candidate subsets. The algorithm is applied in the construction of parsimonious QSAR models based on feed-forward neural networks, and tested on three classical data sets from the QSAR literature. It is shown that the method compares favorably with simulated annealing, and is able to identify a better and more diverse set of solutions given the same amount of simulation time.