Consensus Classification Using Non-Optimized Classifiers

Abstract

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.

Direct Analysis of Human Hair Before and After Cosmetic Modification Using a Recent Data Fusion Method

“…In this study, a recent chemometric method for the classification of samples was applied. 15,25,26 Instead of using an algorithm that tests one by one, this method allows the combination of information from 17 classifiers with no requirements to select a threshold, eigenvector or number of neighbors. In addition, it is possible to easily fuse the data from different instruments.…”

“…The hair samples were cosmetically modified at the laboratory. A recent chemometric method developed by Brownfield et al 15 for classifying these samples without further preprocessing that allowed the easy fusion of the data from LIBS and WDXRF was performed. The fusion of instrumental data has been shown to provide complementarity and improvements in analytical information for many purposes.…”

Big Data Mining Optimization Algorithm Based on Machine Learning Model

“…In view of the shortcomings of the empirical risk minimization strategy and the structural risk minimization strategy, several other methods have been proposed successively to improve overfitting, but they still cannot solve this problem well [11][12][13][14]. Brownfield et al [15] compared seven different nonparametric classifiers -radial basis function neural network, multilayer perceptron neural network, support vector machine, classification and regression tree, Chi-square automatic interaction detection, quick, unbiased and efficient statistical tree algorithm and random forest. The results showed that random forest has the highest accuracy, sensitivity and specificity.…”

Fused adjacency matrices to enhance information extraction: The beer benchmark

“…More recently, a fusion strategy for non-optimized classifiers was proposed, i.e. by considering a window of tuning parameters values for each classifier in the fusion process [22].…”

“…For a given data block X, its corresponding output is the matrix AM X (Equation 3). When more than one X data blocks are available (like in the benchmark case presented in this work, where X = Vis, NIR, NMR), the resulting AM X matrices can be combined using, again, a sum rule ([22], equation 4).The result is the Fused Adjacency Matrix AM Fus , depicted in black inFigure 1. In this work, the values in AM Fus vary between zero and 42, as a result of summing a total of 42 AMs which have ones on their diagonal.…”