Comparison of supervised pattern recognition methods with McNemar’s statistical test
| Autor: | J.P. Huvenne, Ludovic Duponchel, Yves Roggo |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
Learning vector quantization
Artificial neural network business.industry Chemistry 010401 analytical chemistry Regression analysis Pattern recognition 02 engineering and technology 021001 nanoscience & nanotechnology Linear discriminant analysis 01 natural sciences Biochemistry 0104 chemical sciences Analytical Chemistry Probabilistic neural network McNemar's test Pattern recognition (psychology) Environmental Chemistry Artificial intelligence 0210 nano-technology business Spectroscopy Statistical hypothesis testing |
| Zdroj: | Analytica Chimica Acta. 477:187-200 |
| ISSN: | 0003-2670 |
| DOI: | 10.1016/s0003-2670(02)01422-8 |
| Popis: | The application of supervised pattern recognition methodology is becoming important within chemistry. The aim of the study is to compare classification method accuracies by the use of a McNemar’s statistical test. Three qualitative parameters of sugar beet are studied: disease resistance (DR), geographical origins and crop periods. Samples are analyzed by near-infrared spectroscopy (NIRS) and by wet chemical analysis (WCA). Firstly, the performances of eight well-known classification methods on NIRS data are compared: Linear Discriminant Analysis (LDA), K -Nearest Neighbors (KNN) method, Soft Independent Modeling of Class Analogy (SIMCA), Discriminant Partial Least Squares (DPLS), Procrustes Discriminant Analysis (PDA), Classification And Regression Tree (CART), Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ) neural network are computed. Among the three data sets, SIMCA, DPLS and PDA have the highest classification accuracies. LDA and KNN are not significantly different. The non-linear neural methods give the less accurate results. The three most accurate methods are linear, non-parametric and based on modeling methods. Secondly, we want to emphasize the power of near-infrared reflectance data for sample discrimination. McNemar’s tests compare classification developed with WCA or with NIRS data. For two of the three data sets, the classification results are significantly improved by the use of NIRS data. |
| Databáze: | OpenAIRE |
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