Abstract: 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.

Key words: K-Nearest Neighbor（KNN）, classification contribution effective value, incremental learning

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。

关键词: K近邻分类, 分类贡献有效值, 增量学习