One-class classifiers
One-class classifiers
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DOI：
10.1002/cem.1397
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发表时间：
2011-05-01
影响因子：
2.4
通讯作者：
Brereton, Richard G.
中科院分区：
文献类型：
--
作者：
Brereton, Richard G.
介绍了单类分类器的原理，以及单类/多类、软/硬、联合/分离和建模/判别方法的区别。这些方法是通过案例研究来说明的，即从核磁共振代谢组谱分析、聚合物热分析和模拟。描述了两组主要的分类器，即基于统计的质心距离度量(欧几里德距离和二次判别分析)和支持向量域描述(SVDD)。讨论了D统计量的统计基础及其与F统计量、X-2统计量、正态分布和T-2统计量的关系。描述了SVDD D值。概述了估计残差到不相交的主成分模型(Q统计量)的距离的方法，以及它们与基于距离的方法相结合给出的G统计量。版权所有(C)2011 John Wiley&Sons，Ltd.
The principles of one-class classifiers are introduced, together with the distinctions between one-class/multiclass, soft/hard, conjoint/disjoint and modelling/discriminatory methods. The methods are illustrated using case studies, namely from nuclear magnetic resonance metabolomic profiling, thermal analysis of polymers and simulations. Two main groups of classifier are described, namely statistically based distance metrics from centroids (Euclidean distance and quadratic discriminant analysis) and support vector domain description (SVDD). The statistical basis of the D statistic and its relationship with the F statistic, X-2, normal distribution and T-2 is discussed. The SVDD D value is described. Methods for assessing the distance of residuals to disjoint principal component models (Q statistic) and their combination with distance-based methods to give the G statistic are outlined. Copyright (C) 2011 John Wiley & Sons, Ltd.