On-line assurance of interpretability criteria in evolving fuzzy systems – Achievements, new concepts and open issues

In this position paper, we are discussing achievements and open issues in the interpretability of evolving fuzzy systems (EFS). In addition to pure on-line complexity reduction approaches, which can be an important direction for increasing the transparency of the evolved fuzzy systems, we examine the state-of-the-art and provide further investigations and concepts regarding the following interpretability aspects: distinguishability, simplicity, consistency, coverage and completeness, feature importance levels, rule importance levels and interpretation of consequents. These are well-known and widely accepted criteria for the interpretability of expert-based and standard data-driven fuzzy systems in batch mode. So far, most have been investigated only rudimentarily in the context of evolving fuzzy systems, trained incrementally from data streams: EFS have focussed mainly on precise modeling, aiming for models of high predictive quality. Only in a few cases, the integration of complexity reduction steps has been handled. This paper thus seeks to close this gap by pointing out new ways of making EFS more transparent and interpretable within the scope of the criteria mentioned above. The role of knowledge expansion, a peculiar concept in EFS, will be also addressed. One key requirement in our investigations is the availability of all concepts for on-line usage, which means they should be incremental or at least allow fast processing.