Automatic Subclasses Estimation for a Better Classification with HNNP

Ruth Janning, Carlotta Schatten, Lars Schmidt-Thieme

Abstract: Although nowadays many artificial intelligence and especially machine learning research concerns big data, there are still a lot of real world problems for which only small and noisy data sets exist. Applying learning models to those data may not lead to desirable results. Hence, in a former work we proposed a hybrid neural network plait (HNNP) for improving the classification performance on those data. To address the high intraclass variance in the investigated data we used manually estimated subclasses for the HNNP approach. In this paper we investigate on the one hand the impact of using those subclasses instead of the main classes for HNNP and on the other hand an approach for an automatic subclasses estimation for HNNP to overcome the expensive and time consuming manual labeling. The results of the experiments with two different real data sets show that using automatically estimated subclasses for HNNP delivers the best classification performance and outperforms also single state-of-the-art neural networks as well as ensemble methods.