Supervised Classification of Thermal High-Resolution Infrared Images: A Case Study for the Diagnosis of Raynaud's Phenomenon

In many classification problems, relevant features are unknown a priori. Therefore, many candidate features are introduced to represent the phenomenon. Unfortunately, it is often true that most of these are either partially or completely redundant to the target. Thus, when the size of the dataset is [...]
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In many classification problems, relevant features are unknown a priori. Therefore, many candidate features are introduced to represent the phenomenon. Unfortunately, it is often true that most of these are either partially or completely redundant to the target. Thus, when the size of the dataset is large, an important primary step in the classification task is to remove the unwanted features. In this framework, this study proposes a new subset selection algorithm, called JSS+E (Jackknifed Stepwise Selection with Exhaustive search), in order to improve the stepwise selection procedure. The procedure is applied, in a supervised classification approach, for the differential diagnosis of Raynaud's Phenomenon, on the basis of functional infrared (IR) imaging data. The results discussed for a dataset collected at ITAB laboratory in Chieti, allow to refine the experimental protocol in a completely new non-invasive way.