Piece-wise quadratic classifier for multichoice decision environments
Belur V. Dasarathy · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
The concepts underlying two of the common classifier concepts used in multi-choice decision environments, namely the Bayes classifier and the piece-wise linear classifier, are combined in this study to define a piece-wise quadratic classifier. This results in decision surfaces that are complex combinations of the traditional quadratic surfaces defined by the Bayes classifier under the Gaussian assumptions, but would be applicable in environments wherein such Gaussian assumptions may not be truly valid. The paper describes the methodology in detail along with the specifics of the learning and classification algorithms. Experimental results based on standard data sets available in the literature and on the Internet, are included to illustrate the benefits and limitations of the methodology.