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Selecting wavelet transform scales for multivariate classification
Author(s) -
Woody Nathaniel A.,
Brown Steven D.
Publication year - 2007
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/cem.1060
Subject(s) - pattern recognition (psychology) , preprocessor , wavelet transform , classifier (uml) , wavelet , artificial intelligence , discrete wavelet transform , computer science , multivariate statistics , constant q transform , harmonic wavelet transform , machine learning
Abstract The wavelet transform is a relatively new method that partitions a signal into components which differ in the frequency of their features. Chemical data can be thought of as a signal being composed of several different frequency components, some more analytically significant than others. Therefore, a wavelet transform can be used to transform chemical data into components, called scales, which differ in the underlying frequency of the signal. These scales possess different signal to noise ratios, or alternatively contain different amounts of information, than the original data. In this manuscript, two classification problems are used to demonstrate how this property can be exploited to improve classification performance. We demonstrate that the classification performance of the individual scales varies, and we show that selectively combining scales results in a classifier with better performance than that of either a classifier trained on the original data or developed on any individual scale. This analysis demonstrates the applicability of the wavelet transform as a simple preprocessing step that can improve classification performance. Copyright © 2007 John Wiley & Sons, Ltd.