A CLASS-MODELING TECHNIQUE BASED ON POTENTIAL FUNCTIONS

A probabilistic and distribution-free class-modelling technique is developed from potential function discriminant analysis. In the multidimensional space of variables the class boundary is built either by the sample percentile of the probability density estimated by means of potential functions, or by the estimate of the 'equivalent' determinant of the variance-covariance matrix. The equivalent determinant is that of a hypothetical multivariate normal distribution whose mean probability density was obtained by potential functions. The bases of this modelling rule are evaluated by means of Monte Carlo experiments. The results on four datasets are used to measure the performances of this method, which equal and sometimes exceed the performances of parametric class-modelling methods based on linear and quadratic discriminant analysis which were used for comparison.