NONPARAMETRIC CLASSIFIER DESIGN USING GREEDY TREE-STRUCTURED VECTOR QUANTIZATION TECHNIQUE

In this paper, we propose a novel tree-structured vector quantization (TSVQ) design algorithm for the applications of nonparametric pattern recognition. The TSVQ design algorithm is used to reduce the large siz e of the design sets required by a nonparametric classifier. For an N- class problem, the TSVQ consists of N branches with one for each class . Using the design sets as training data, the algorithm splits the lea f nodes in a greedy manner to minimize the classification error rate f or tree-growing. Simulation results show that the classifiers designed using this new algorithm require less classification time than that r equired by other design set reduction algorithms. In addition, in many cases, the new classifiers enjoy almost the same low error rate as th at of traditional k-NN nonparametric classifiers. (C) 1997 Published b y Elsevier Science B.V.