A first attempt on evolutionary prototype reduction for nearest neighbor one-class classification

Evolutionary prototype reduction techniques are data preprocessing methods originally developed to enhance the nearest neighbor rule. They reduce the training data by selecting or generating representative examples of a given problem. These algorithms have been designed and widely analyzed in standard classification providing very competitive results. However, its application scope can be extended to many other specific domains, such as one-class classification, in which its way of working is very interesting in order to reduce computational complexity and sensitivity to noisy data. In this contribution, we perform a first study on the usefulness of evolutionary prototype reduction methods for one-class classification. To do so, we will focus on two recent evolutionary approaches that follow very different strategies: selection and generation of examples from the training data. Both alternatives provide a resulting preprocessed data set that will be used later by a nearest neighbor one-class classifier as its training data. The results achieved support that these data reduction techniques are suitable tools to improve the performance of the nearest neighbor one-class classification.