In this paper we present a novel model originating from Takagi-Sugeno fuzzy models. It is based on a concept of transductive similarity, where unlike a simple inductive similarity, it considers also local neighborhood of a given element. Transductive property of a local space is used in an inference process, what allows the technique to be used also in incremental settings. Since incremental model construction brings new challenges, we are unable to use the offline transductive approach as some of the previous works did. The key idea of our model is to adjust activation properties of each rule, based on cross-rule similarities. Our method is capable of using the transductive property for any metric. Besides the final model, we also present several improvements to the transductive similarity technique itself, where we alternate the similarity metric in several ways to better exploit the influence of local neighborhood in the final metric. At the end, we demonstrate a superior performance of our technique over the state-of-the-art techniques build on TS fuzzy models on several machine learning datasets. 
TITS-FM: Transductive incremental Takagi-Sugeno fuzzy models