PRINCIPAL FEATURE CLASSIFICATION

The concept, structures, and algorithms of principal feature classific ation (PFC) are presented in this paper. PFC is intended to solve comp lex classification problems with large data sets. A PFC network is des igned by sequentially finding principal features and removing training data which has already been correctly classified. PFC combines advant ages of statistical pattern recognition, decision trees, and artificia l neural networks (ANN's) and provides fast learning with good perform ance and a simple network structure. For the real-world applications o f this paper, PFC provides better performance than conventional statis tical pattern recognition, avoids the long training times of backpropa gation and other gradient-decent algorithms for ANN's, and provides a low-complexity structure for realization.