A MULTISTAGE GENERALIZATION OF THE RANK NEAREST-NEIGHBOR CLASSIFICATION RULE

被引：10

作者：

BAGUI, SC ^{[1
]}

PAL, NR ^{[1
]}

机构：

[1] UNIV W FLORIDA,DIV COMP SCI,PENSACOLA,FL 32514

来源：

PATTERN RECOGNITION LETTERS | 1995年 / 16卷 / 06期

关键词：

BAYES ERROR RATE; CLASSIFICATION; RANK NEAREST NEIGHBOR; KAPPA-NEAREST NEIGHBOR;

D O I：

10.1016/0167-8655(95)80006-F

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We consider the problem of classifying an unknown observation from one of s (greater than or equal to 2) univariate classes (or populations) using a multi-stage left and right rank nearest neighbor (RNN) rule. We derive the asymptotic error rate (i.e., total probability of misclassification (TPMC)) of the m-stage univariate RNN (m-URNN) rule, and show that as the number of stages increases, the limiting TPMC of the m-stage univariate rule decreases. Monte Carlo simulations are used to study the behavior of the m-URNN rule and compare it with the conventional R-NN rule. Finally, we incorporate an extension of the m-URNN rule to multivariate observations with empirical results.

引用

页码：601 / 614

页数：14

共 14 条

[1]

ANDERSON TW, 1966, 1ST P INT S AN

[2] CLASSIFICATION USING 1ST-STAGE RANK NEAREST-NEIGHBOR RULE FOR MULTIPLE CLASSES [J].