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Consensus Classification Using Non-Optimized Classifiers
Analytical Chemistry ( IF 7.4 ) Pub Date : 2018-03-05 00:00:00 , DOI: 10.1021/acs.analchem.7b04399
Brett Brownfield 1 , Tony Lemos 1 , John H. Kalivas 1
Affiliation  

Classifying samples into categories is a common problem in analytical chemistry and other fields. Classification is usually based on only one method, but numerous classifiers are available with some being complex, such as neural networks, and others are simple, such as k nearest neighbors. Regardless, most classification schemes require optimization of one or more tuning parameters for best classification accuracy, sensitivity, and specificity. A process not requiring exact selection of tuning parameter values would be useful. To improve classification, several ensemble approaches have been used in past work to combine classification results from multiple optimized single classifiers. The collection of classifications for a particular sample are then combined by a fusion process such as majority vote to form the final classification. Presented in this Article is a method to classify a sample by combining multiple classification methods without specifically classifying the sample by each method, that is, the classification methods are not optimized. The approach is demonstrated on three analytical data sets. The first is a beer authentication set with samples measured on five instruments, allowing fusion of multiple instruments by three ways. The second data set is composed of textile samples from three classes based on Raman spectra. This data set is used to demonstrate the ability to classify simultaneously with different data preprocessing strategies, thereby reducing the need to determine the ideal preprocessing method, a common prerequisite for accurate classification. The third data set contains three wine cultivars for three classes measured at 13 unique chemical and physical variables. In all cases, fusion of nonoptimized classifiers improves classification. Also presented are atypical uses of Procrustes analysis and extended inverted signal correction (EISC) for distinguishing sample similarities to respective classes.

中文翻译:

使用非优化分类器进行共识分类

将样品归类是分析化学和其他领域的常见问题。分类通常仅基于一种方法,但是可以使用许多分类器,其中一些分类器很复杂,例如神经网络,而另一些分类器很简单,例如k个最近的邻居。无论如何,大多数分类方案都需要优化一个或多个调整参数,以实现最佳的分类精度,灵敏度和特异性。不需要精确选择调整参数值的过程将很有用。为了改进分类,过去的工作中使用了几种集成方法来组合来自多个优化的单个分类器的分类结果。然后,通过诸如多数表决之类的融合过程将特定样本的分类集合合并,以形成最终分类。本文介绍的是一种通过组合多种分类方法对样本进行分类的方法,而无需通过每种方法对样本进行具体分类,也就是说,分类方法并未得到优化。在三个分析数据集上演示了该方法。第一个是啤酒认证集,其中包含在五种仪器上测量的样本,从而允许通过三种方式融合多种仪器。第二个数据集由基于拉曼光谱的三类纺织品样本组成。此数据集用于演示使用不同的数据预处理策略同时进行分类的能力,从而减少了确定理想的预处理方法(准确分类的常见先决条件)的需要。第三个数据集包含三个类别的三个葡萄酒品种,分别在13个独特的化学和物理变量下进行了测量。在所有情况下,未优化分类器的融合都会改善分类。还介绍了Procrustes分析和扩展的倒置信号校正(EISC)在区分样本相似性到各个类别方面的非典型用法。
更新日期:2018-03-05
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