Abstract
- Machine learning classifiers adjust implicitly or explicitly the importance of the data features to solve a given classification task. This feature weighting often does not imply feature sparseness, which, however, may be important for interpretability and model evaluation. This contribution proposes how to force feature sparseness in combination with feature relevance for prototype-based classification learning to obtain reliable and interpretable classification decisions.