摘要： 提出了用于增强kNN的属性Bagging(ABagging),ABagging通过对属性重抽样而不是对训练实例重抽样来获得多个训练集。kNN对于属性重抽样不稳定,因而ABagging能有效降低kNN的错误率。ABaggingkNN对于不相关属性也有比kNN强得多的抵抗力。另外AbaggingkNN的速度也比BaggingkNN更快。用UCI数据集证明了ABaggingkNN的有效性。

关键词: Bagging, kNN, 属性Bagging

Abstract: This paper proposes the attributes Bagging to improve KNN. A simple alternative to bagging is to resampling attribute sets instead oftraining sets. Attributes bagging kNN results in lower error rate than that of kNN because kNN is unstable when attributes bagging is employed.Furthermore attributes Bagging kNN is more robust to irrelevant attributes than kNN itself. Finally attributes bagging kNN is faster than BaggingkNN. Experiments with UCI datasets show the validity of attributes Bagging kNN.

中图分类号: 

• TP181