Abstract
The classification of wounds into healing states depending on their absorption spectrum of visible and near infrared light remains an important task in dermatology. Moreover, a reduction of the spectrum that is used in the classification task to fewer but important wavelengths is desirable, as each measured wavelength increases the examination costs without necessarily providing further information to the classification of wound healing states. This paper addresses two aspects: First the improvement of the classification of wounds into healing states and second, a cost reduction by choosing only important wavelengths. Standard Data Mining methods are evaluated for their classification accuracy (CA) and compared to their performance when applying feature selection techniques that are used to reduce the amount of necessary wavelengths. The results indicate that the 1-nearest-neighbor approach (IB1 algorithm) comes up with the best CA, while only relying on a fraction (4%) of the standard wavelength spectrum.