Hierarchical Fuzzy Inductive Reasoning Classifier

Many of the inductive reasoning algorithms and techniques, including Fuzzy Inductive Reasoning (FIR), that learn from labelled data don't provide the possibility of involving domain expert knowledge to induce rules. In those cases that learning fails, this capability can guide the learning mechanism towards a hypothesis that seems more promising to a domain expert. One of the main reasons for omitting such involvement is the difficulty of knowledge acquisition from experts and, also, the difficulty of combining it with induced hypothesis. One of the successful solutions to such a problem is an alternative approach in machine learning called Argument Based Machine Learning (ABML) which involves experts in providing specific explanations in the form of arguments to only specific cases that fail, rather than general knowledge on all cases. Inspired by this study, the idea of Hierarchical Fuzzy Inductive Reasoning (HFIR) is proposed in this paper as the first step towards design and development of an Argument Based Fuzzy Inductive Reasoning method capable of providing domain expert involvement in its induction process. Moreover, HFIR is able to obtain better classifications results than classical FIR methodology. In this work, the concept of Hierarchical Fuzzy Inductive Reasoning is introduced and explored by means of the Zoo UCI benchmark.