Purpose of this work is to show that the Particle Swarm Optimization Algorithm may improve the results of some well known Machine Learning methods in the resolution of discrete classification problems. A binary version of the PSO algorithm is used to obtain a set of logic rules that map binary masks (that represent the attribute values), to the available classes. This algorithm has been tested both in a single pass mode and in an iterated mode on a well-known set of problems, called the MONKS set, to compare the PSO results against the results reported for that domain by the application of some common Machine Learning algorithms.
This paper introduces a new sensitivity analysis method using nonsupervised neural nets, based on the Adaptive Resonance Theory (ART).This new method introduces the possibility of a sensitivity analysis being adaptive and being conducted at the saine tirne as net learning is taking place, taking advantage of the property of continuous (as opposed to phase-wise) learning of ART models. A sensitivity analysis can be conducted likewise, i.e. continuous by and capably adapting to any new relationships appearing among the input data. The method has been validated in the field of feature detection for iniage classification and, more specifically, for face recognition.