  We implement extensions of the partial least squares generalized linear
regression (PLSGLR) due to Bastien et al. (2005) through its combination with
logistic regression and linear discriminant analysis, to get a partial least
squares generalized linear regression-logistic regression model (PLSGLR-log),
and a partial least squares generalized linear regression-linear discriminant
analysis model (PLSGLRDA). These two classification methods are then compared
with classical methodologies like the k-nearest neighbours (KNN), linear
discriminant analysis (LDA), partial least squares discriminant analysis
(PLSDA), ridge partial least squares (RPLS), and support vector machines(SVM).
Furthermore, we implement the kernel multilogit algorithm (KMA) by Dalmau et
al. (2015)and compare its performance with that of the other classifiers. The
results indicate that for both un-preprocessed and preprocessed data, the KMA
has the lowest classification error rates.
