Towards Dynamic Fuzzy Interpolation Based on Rule Assessment

Fuzzy Rule Interpolation (FRI) provides a useful mechanism to derive reasonable approximate inference outcomes when presented with a sparse rule base. However, conventional FRI techniques are exploited to generate fuzzy conclusions for observations that have no matching rules in a static rule base. Any interpolated rules created during the interpolation process are abandoned once the interpolated results are obtained, despite they may contain valuable, and general or generalisable, information about the domain problem. This paper proposes an approach that helps improve the sparse rule base dynamically, through measuring and subsequently adding certain interpolated rules that are deemed of high value into the sparse rule base. In particular, the value of an interpolated rule is assessed via its similarities with those given in the sparse rule base, in an effort to determine whether it is to be incorporated in the existing rule base. Experimental results on benchmark datasets illustrate that by the use of a dynamically enriched rule base the popular transformation-based FRI algorithm is able to produce strengthened inference outcomes over those that it generates while using just the original static rule base.