Adapt Bagging to Nearest Neighbor Classifiers  

作　　者：Zhi-HuaZhou YangYu 

机构地区：[1]NationalLaboratoryforNovelSoftwareTechnology,NanjingUniversity,Nanjing210093,P.R.China

出　　处：《Journal of Computer Science & Technology》2005年第1期48-54,共7页计算机科学技术学报（英文版）

基　　金：国家杰出青年科学基金，the Fok Ying Tung Education Foundation，教育部优秀青年教师资助计划

摘　　要：It is well-known that in order to build a strong ensemble, the component learners should be with high diversity as well as high accuracy. If perturbing the training set can cause significant changes in the component learners constructed, then Bagging can effectively improve accuracy. However, for stable learners such as nearest neighbor classifiers, perturbing the training set can hardly produce diverse component learners, therefore Bagging does not work well. This paper adapts Bagging to nearest neighbor classifiers through injecting randomness to distance metrics. In constructing the component learners, both the training set and the distance metric employed for identifying the neighbors are perturbed. A large scale empirical study reported in this paper shows that the proposed BagInRand algorithm can effectively improve the accuracy of nearest neighbor classifiers.It is well-known that in order to build a strong ensemble, the component learners should be with high diversity as well as high accuracy. If perturbing the training set can cause significant changes in the component learners constructed, then Bagging can effectively improve accuracy. However, for stable learners such as nearest neighbor classifiers, perturbing the training set can hardly produce diverse component learners, therefore Bagging does not work well. This paper adapts Bagging to nearest neighbor classifiers through injecting randomness to distance metrics. In constructing the component learners, both the training set and the distance metric employed for identifying the neighbors are perturbed. A large scale empirical study reported in this paper shows that the proposed BagInRand algorithm can effectively improve the accuracy of nearest neighbor classifiers.

关 键 词：BAGGING data mining ensemble learning machine learning Minkowsky distance nearest neighbor value difference metric 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]