Nearest Neighbor Classification Rule for Mixtures of Discrete and Continuous Random Variables
T. J. Wojciechowski · Biometrical Journal · 1987
Abstract In this paper very simple nonparametric classification rule for mixtures of discrete and continuous random variables is described. It is based on the method of nearest neighbor proposed by Cover and Hart (1967). The bounds on the limit of the nearest neighbor rule risks are given. Both lower and upper bound depend on the Bayes risk and the loss function. Finally the method is compared with other existing methods on some practical data set.