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A variable selection strategy for supervised classification with continuous spectroscopic data
Author(s) -
Indahl Ulf,
Næs Tormod
Publication year - 2004
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.836
Subject(s) - benchmark (surveying) , selection (genetic algorithm) , linear discriminant analysis , set (abstract data type) , feature selection , discriminant , variable (mathematics) , data set , computer science , mathematics , pattern recognition (psychology) , continuous variable , power (physics) , artificial intelligence , data mining , algorithm , statistics , physics , geography , mathematical analysis , geodesy , programming language , quantum mechanics
Abstract In this paper we present a new variable selection method designed for classification problems where the X data are discretely sampled from continuous curves. For such data the loading weight vectors of a PLS discriminant analysis inherit the continuous behaviour, making the idea of local peaks meaningful. For successive components the local peaks are checked for importance before entering the set of selected variables. Our examples with NIR/NIT show that substantial simplification of the X space can be obtained without loss of classification power when compared with ‘benchmark full‐spectrum’ methods. Copyright © 2004 John Wiley & Sons, Ltd.