Abstract
This paper proposes a method for building accurate and interpretable systems by integrating expert and induced knowledge into a single knowledge base. To favor the cooperation between expert knowledge and data, the induction process is run under severe constraints to ensure the fully control of the expert. The procedure is made up of two hierarchical steps. Firstly, a common fuzzy input space is designed according to both the data and expert knowledge. The compatibility of the two types of partitions, expert and induced, is checked according to three criteria : range, granularity and semantic interpretation. Secondly, expert rules and induced rules are generated according to the previous common fuzzy input space. Then, induced and expert rules have to be merged into a new rule base. Thanks to the common universe resulting from the first step, rule comparison can be made at the linguistic level only. The possible conflict situations are managed and the most important rule base features, consistency, redundancy and completeness, are studied. The first step is thoroughly described in this paper, while the second is only introduced.
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