计算机工程与应用2012，Vol.48Issue(3)：185-188,4.DOI:10.3778/j.issn.1002-8331.2012.03.055
基于分类贡献有效值的增量KNN模型修剪研究
Research on incremental KNN model pruning based on classification contribution effective value
周靖 1刘晋胜1
作者信息
- 1. 广东石油化工学院计算机与电子信息学院,广东茂名525000
- 折叠
摘要
Abstract
The effect of incremental learning impacts on the efficiency and the rate of AT-Nearest Neighbor algorithm directly. An incremental ANN model based on contribution effective value (CEV-KNNMODEL) is proposed, the paper combines the classification contribution degree and ANN incremental learning, defines a new contribution effective value of the training sample, and formulates the training set pruning strategy according to this definition. The theory and experiment shows that the applicability of CEV-KNNMODEL is strong, and the performance of the classifier can be greatly improved.关键词
K近邻分类/分类贡献有效值/增量学习Key words
K-Nearest Neighbor(ANN)/ classification contribution effective value/ incremental learning分类
信息技术与安全科学引用本文复制引用
周靖,刘晋胜..基于分类贡献有效值的增量KNN模型修剪研究[J].计算机工程与应用,2012,48(3):185-188,4.
