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2.2.4 Ensemble of Classifiers
In an effort to enhance the overall accuracy ( ) of the classifiers, while
simultaneously control the bias-variance trade-off and minimise the instances of
overfitting (see Section 1.6.5), the use of ensembles of classifiers was also evaluated.
1. Selection of the Classification Model
First and foremost, the selection of the classification model to be applied had to be
decided upon. There is no straightforward way of determining a priori which
classification algorithm is the best; the selection of a classification model or kernel
function highly depends on the problem under investigation. In cases where there is
very little or no knowledge about the data under study, often more than one type of
classifier may need to be tested. The choice of the classifier determines the
hyperparameters to be optimised. In the case of PLS-DA, we are looking for the
optimum number of latent variables (LVs), whereas in the case of RBF SVMs, the
hyperparameters and have to be optimised.
2. Random split
For a given input dataset D, a random fraction of samples is removed and kept aside
as an independent test set during the training process of the model. This selection of
samples forms the dataset D test . This test set typically comprises a third of the original
samples. Using a stratified holdout approach as described in Section 1.6.1, the test set
consists of the same balance of sample classes as the initial dataset D. The remaining
samples that are not selected, form the training set D train . Since the test set is kept
aside during the whole training process, the risk of overfitting is minimised (Ramadan
et al., 2006).
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