Interpretable Classifiers in Precision Medicine: Feature Selection and Multi-class Categorization
- Cite this paper as:
- Schirra LR., Schmid F., Kestler H.A., Lausser L. (2016) Interpretable Classifiers in Precision Medicine: Feature Selection and Multi-class Categorization. In: Schwenker F., Abbas H., El Gayar N., Trentin E. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2016. Lecture Notes in Computer Science, vol 9896. Springer, Cham
Growing insight into the molecular nature of diseases leads to the definition of finer grained diagnostic classes. Allowing for better adapted drugs and treatments this change also alters the diagnostic task from binary to multi-categorial decisions. Keeping the corresponding multi-class architectures accurate and interpretable is currently one of the key tasks in molecular diagnostics.
In this work, we specifically address the question to which extent biomarkers that characterize pairwise differences among classes, correspond to biomarkers that discriminate one class from all remaining. We compare one-against-one and one-against-all architectures of feature selecting base classifiers. They are validated for their classification performance and their stability of feature selection.