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Combined principal component preprocessing and n ‐tuple neural networks for improved classification
Author(s) -
Linneberg Christian,
Höskuldsson Agnar
Publication year - 2000
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/1099-128x(200009/12)14:5/6<573::aid-cem617>3.0.co;2-y
Subject(s) - principal component analysis , preprocessor , artificial neural network , artificial intelligence , multiclass classification , computer science , classifier (uml) , pattern recognition (psychology) , tuple , data mining , data pre processing , transformation (genetics) , machine learning , mathematics , support vector machine , biochemistry , chemistry , discrete mathematics , gene
We present a combined principal component analysis/neural network scheme for classification. The data used to illustrate the method consist of spectral fluorescence recordings from seven different production facilities, and the task is to relate an unknown sample to one of these seven factories. The data are first preprocessed by performing an individual principal component analysis on each of the seven groups of data. The components found are then used for classifying the data, but instead of making a single multiclass classifier, we follow the ideas of turning a multiclass problem into a number of two‐class problems. For each possible pair of classes we further apply a transformation to the calculated principal components in order to increase the separation between the classes. Finally we apply the so‐called n ‐tuple neural network to the transformed data in order to give the classification system non‐linear capabilities, and all derived two‐class models are combined to facilitate multiclass classification. Validation results show that the combined scheme is superior to the individual methods. Copyright © 2000 John Wiley & Sons, Ltd.