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N3 和 BNN:两种新的基于相似度的分类方法与其他分类器的比较。

N3 and BNN: Two New Similarity Based Classification Methods in Comparison with Other Classifiers.

机构信息

Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca , P.zza della Scienza, 1, 20126 Milan, Italy.

出版信息

J Chem Inf Model. 2015 Nov 23;55(11):2365-74. doi: 10.1021/acs.jcim.5b00326. Epub 2015 Nov 2.

Abstract

Two novel classification methods, called N3 (N-nearest neighbors) and BNN (binned nearest neighbors), are proposed. Both methods are inspired by the principles of the K-nearest neighbors (KNN) method, being both based on object pairwise similarities. Their performance was evaluated in comparison with nine well-known classification methods. In order to obtain reliable statistics, several comparisons were performed using 32 different literature data sets, which differ for number of objects, variables and classes. Results highlighted that N3 on average behaves as the most efficient classification method with similar performance to support vector machine based on radial basis function kernel (SVM/RBF). The method BNN showed on average higher performance than the classical K-nearest neighbors method.

摘要

提出了两种新的分类方法,分别称为 N3(N-最近邻)和 BNN(分箱最近邻)。这两种方法都是受 K-最近邻(KNN)方法的原理启发,都是基于对象对之间的相似性。为了评估它们的性能,将它们与九种著名的分类方法进行了比较。为了获得可靠的统计数据,使用了 32 个不同的文献数据集进行了多次比较,这些数据集在对象数量、变量和类别上有所不同。结果表明,N3 平均表现为最有效的分类方法,其性能与基于径向基函数核的支持向量机(SVM/RBF)相似。BNN 方法的平均性能优于经典的 KNN 方法。

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