摘要
许多传统分类算法都以训练数据和测试数据具有相同或至少非常相似的分布为前提,但是在实际应用中,该前提很难得到保证,这降低支持向量机等传统分类算法的分类精度.因此,文中提出基于类标感知的KNN分类算法(CA-KNN).CA-KNN给出稀疏表示模型,基于任何测试数据都可使用训练数据集进行稀疏表示的假设.CA-KNN可有效利用数据集上的类标信息,提升稀疏表示的准确性.引入KNN的最近邻分类思想,进一步提升CA-KNN的泛化能力,并且从理论上证明CA-KNN分类器与最小误差的Bayes决策规则关联.实验和理论分析的结果表明,CA-KNN具有较好的分类性能.
Many conventional classification methods start with the hypothesis that the distribution of training samples is same as or at least similar to that of testing samples.In many practical applications,it is difficult to agree with the above hypothesis.And thus the classification performance of some traditional methods,such as support vector machine,is reduced.Therefore,a class-aware based KNN classification method(CA-KNN)is proposed.A sparse representation model is proposed based on the assumption that any testing sample can be represented sparsely by the training samples.The class label information is utilized effectively by CA-KNN to improve the accuracy of the sparse representation.The idea of nearest neighbor classification of KNN is introduced to improve the generalization capability of CA-KNN.And it is proved in theory that CA-KNN classifier is directly related to Bayes decision rule for the minimum error.The experimental and theoretical results show that CA-KNN generates better classification performance.
作者
卞则康
张进
王士同
BIAN Zekang;ZHANG Jin;WANG Shitong(School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122;Jiangsu Key Construction Laboratory of Internet of Things App-lication Technology,Wuxi Taihu University,Wuxi 214064)
出处
《模式识别与人工智能》
CSCD
北大核心
2021年第10期873-884,共12页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61972181,61772198)
江苏省自然科学基金项目(No.BK20191331)
江苏省物联网应用技术重点建设实验室2020年度开放课题(No.WXWL01)资助。
关键词
类标感知
稀疏表示
K近邻分类
最小误差的Bayes决策规则
Class-Aware
Sparse Representation
K Nearest Neighbor Classification
Bayes Decision Rule for Minimum Error
