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

作　　者：周靖[1] 刘晋胜[1] 

机构地区：[1]广东石油化工学院计算机与电子信息学院,广东茂名525000

出　　处：《计算机工程与应用》2012年第3期185-188,共4页Computer Engineering and Applications

摘　　要：增量学习的效果直接影响到KNN的效率和准确率。提出基于分类贡献有效值的增量KNN修剪模型(C2EV-KNNMODEL),将特征参数的分类贡献度与KNN增量学习结合起来,定义一种新的训练样本的贡献有效值,并根据此定义制定训练集模型的修剪策略。理论和实验表明,C2EV-KNNMODEL的适用性较强,能够使分类器的分类性能得到极大的提高。The effect of incremental learning impacts on the efficiency and the rate of K-Nearest Neighbor algorithm directly.An incremental KNN model based on contribution effective value（CEV-KNNMODEL）is proposed,the paper combines the classification contribution degree and KNN 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 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]