TY - JOUR
T1 - Nonparametric classifier design using greedy tree-structured vector quantization technique
AU - Hwang, Wen Jyi
AU - Ye, Bo Yuan
AU - Lai, Lin Ying
PY - 1997/5
Y1 - 1997/5
N2 - 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 size 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 leaf nodes in a greedy manner to minimize the classification error rate for tree-growing. Simulation results show that the classifiers designed using this new algorithm require less classification time than that required by other design set reduction algorithms. In addition, in many cases, the new classifiers enjoy almost the same low error rate as that of traditional k-NN nonparametric classifiers.
AB - 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 size 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 leaf nodes in a greedy manner to minimize the classification error rate for tree-growing. Simulation results show that the classifiers designed using this new algorithm require less classification time than that required by other design set reduction algorithms. In addition, in many cases, the new classifiers enjoy almost the same low error rate as that of traditional k-NN nonparametric classifiers.
KW - k-nearest-neighbor (kNN) classifier
KW - Nonparametric classification
KW - Vector quantization
UR - http://www.scopus.com/inward/record.url?scp=0031141071&partnerID=8YFLogxK
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U2 - 10.1016/S0167-8655(97)00036-6
DO - 10.1016/S0167-8655(97)00036-6
M3 - Article
AN - SCOPUS:0031141071
VL - 18
SP - 409
EP - 414
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
SN - 0167-8655
IS - 5
ER -