2.3.3. Optimization procedure

Several settings of the algorithm described in the previous chapter were mentioned but not quantified. The values of those settings were found by an automatic optimization procedure. The automatic optimization procedure was a gradient descent process which maximized the area under the receiver operating characteristic curve (details in chapter 2.5).

This type of optimization belongs to a subcategory of machine learning procedures called supervised learning procedures. Those processes require labeled data for the optimization / training process. It is good practice to do optimization, threshold selection and testing with separate datasets, where no data in one dataset can be found in another. The difference between performance metrics of two datasets is a measure of the degree the algorithm or the threshold selection is overfitted to a dataset. The labeling / classification process, the datasets and the performance metrics are described in the following chapters.