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  • Cross-validation  (2)
  • Recall  (2)
  • SIMCA  (2)
  • Pyrolysis mass spectra  (1)
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  • 1
    ISSN: 0886-9383
    Schlagwort(e): Pattern recognition ; SIMCA ; PCA ; Classification ; Bacteria ; Pyrolysis mass spectra ; Recall ; Cross-validation ; Indicator function ; Pseudo random data sets ; Leave-x-out ; Chemistry ; Analytical Chemistry and Spectroscopy
    Quelle: Wiley InterScience Backfile Collection 1832-2000
    Thema: Chemie und Pharmazie
    Notizen: As part of a critical evaluation of the pattern recognition method SIMCA, three data sets containing pyrolysis mass spectra from bacteria were analysed using the SIMCA classifier. Each set consisted of two classes, Pseudomonas and Serratia bacteria, each class containing ten mass spectra and each mass spectrum having 285 spectral features.The results indicate that for these py-MS data sets, with low object/feature ratio, the SIMCA classifier produces satisfactory results at the first classification level. At the second level, however, the classification results are not reliable, even after deleting outliers. A comparison of the cross-validation method and Malinowski's indicator function for the determination of the number of significant principal components showed that the cross-validation method is less stable and therefore less reliable than the indicator function.
    Zusätzliches Material: 11 Tab.
    Materialart: Digitale Medien
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    Digitale Medien
    Digitale Medien
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 1 (1987), S. 221-230 
    ISSN: 0886-9383
    Schlagwort(e): Pattern recognition ; SIMCA ; PCA ; Classification ; Recall ; Cross-validation ; Indicator function ; Leave-x-out ; Pseudo random data sets ; Chemistry ; Analytical Chemistry and Spectroscopy
    Quelle: Wiley InterScience Backfile Collection 1832-2000
    Thema: Chemie und Pharmazie
    Notizen: The SIMCA pattern recognition method has been evaluated with pseudo random data sets. The number of objects varied from 5 to 50 and the number of features from 5 to 300.First, the determination of the significant number of PCs in the SIMCA models by the cross-validation method was compared with the indicator function. The results showed that for the lower dimensions (≤ 15 objects or ≤ 15 features) the indicator function produces more reliable results.Second, the classification results with SIMCA were analysed for data sets with two equally sized classes and a varying number of objects and features, using the recall function as the evaluation criterion. The results showed that the SIMCA classifier produces reliable results at the first classification level, even for a low object/feature ratio (5/300). However, at the second level the classification performance of SIMCA decreases rapidly with an increasing number of features, even when the data set consists of two very well separated classes and little random error.
    Zusätzliches Material: 6 Ill.
    Materialart: Digitale Medien
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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