Session Program

Abstract - This paper presents FISDeT, a Python tool that enables the design of a Fuzzy Inference System (FIS) based on the standard language FCL. FISDeT includes a GUI that enables the user to easily define and update the rule base of a FIS. Given a FIS, the tool can perform the inference of fuzzy rules. To show the main features of FISDeT, in this paper we employ the tool to develop fuzzy rule-based systems that can solve the problem of beer style classification. The integrated testing facilities of FISDeT enable a comparison among the created classifiers.

Abstract - This paper presents an empirical research. It focuses on testing empirically the benefits of providing users, in a specific domain, with textual interpretation of the fuzzy inferences carried out by a fuzzy classifier for a given selection of samples. The hypothesis to test is as follows: ``Users understand easier the decision made by a fuzzy system when they are provided with a textual interpretation of the fuzzy inference mechanism that the system carried out''. This hypothesis was successfully tested in a web survey. The application domain was leaf classification. The fuzzy classifiers were built with the GUAJE fuzzy modeling open source software which is aimed at generating interpretable fuzzy systems. The textual interpretation was handmade by an expert who followed the guidelines of the Natural Language Generation approach proposed by Reiter and Dale. Reported results encourage us to go on with a series of additional experiments devoted to deeply explore how Natural Language Generation techniques can contribute to facilitate the understanding of fuzzy systems.

Abstract - The existing studies of the interpretability of fuzzy systems have mainly focused on the analysis of the relation between a model of the fuzzy system and a human user considered as a beneficiary, i.e., a domain expert or a designer. Suggested by the concepts of "model" of formalized theories, "realization" of formalized language, and "interpretability" of a theory in another, the main contribution of this paper is the proposal of a new approach to the fuzzy system interpretability. This is grounded on the qualitative real-world semantics of words and relationships between the semantics of fuzzy system components and substructures of their real-world counterparts. Thus, we introduce a novel real-world-semantics-based approach. It is aimed at characterizing the so-called real-world-semantics-based interpretability of fuzzy systems. In addition, it considers the actual semantics of all fuzzy system components, including the inference engine. Moreover, this new approach opens the door to a new way to study the interpretability of fuzzy systems.

Abstract - DC* is a method for generating interpretable fuzzy information granules from pre- classified data. It is based on the subsequent application of LVQ1 for data compression and an ad-hoc procedure based on A* to represent data with the minimum number of fuzzy information granules satisfying some interpretability constraints. While being efficient in tackling several problems, the A* procedure included in DC* may happen to require a long computation time because the A* algorithm has exponential time complexity in the worst case. In this paper, we approach the problem of driving the search process of A* by suggesting a close- to-optimal solution that is produced through a Genetic Algorithm (GA). Experimental evaluations show that, by driving the A* algorithm embodied in DC* with a GA solution, the time required to perform data granulation can be reduced by at least 45\% and up to 99\%.

Abstract - Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve interpretability of fuzzy logic systems (FLSs). In recent years, a variety of indices have been proposed to measure the interpretability of FLSs such as the Nauck index and Fuzzy index. However, interpretability indices associated with HFSs have not so far been discussed. The structure of HFSs, with multiple layers, subsystems, and varied topologies, is the main challenge in constructing interpretability indices for HFSs. Thus, the comparison of interpretability between FLSs and HFSs--even at the index level --is still subject to open discussion. This paper begins to address these challenges by introducing extensions to the FLS Nauck and Fuzzy interpretability indices for HFSs. Using the proposed indices, we explore the concept of interpretability in relation to the different structures in FLSs and HFSs. Initial experiments on benchmark datasets show that based on the proposed indices, HFSs with equivalent function to FLSs produce higher indices, i.e. are more interpretable than their corresponding FLSs.

Abstract - The study of human remains suffers from a lack of information for determining a reliable estimation of the age of an individual. One of the most extended methods for this task was proposed in the twenties of the past century and is based on the analysis of the pubic bone. The method describes some age changes occurring in the pubic bone and establishes ten different age ranges with a description of the morphological aspect of the bone in each one of them. These descriptions are sometimes vague and there is not a systematic way for using the method. In this contribution we propose two different preliminary fuzzy rule-based classification system designs for age estimation from the pubic bone that consider the main morphological characteristics of the bone as independent and linguistic variables. So, we have identified the problem variables and we have defined the corresponding linguistic labels making use of forensic expert knowledge, that is also considered to design a decision support fuzzy system. A brief collection of pubic bones labeled by forensic anthropologists has been used for learning the second fuzzy rule-based classification system by means of a fuzzy decision tree. The experiments developed report a best performance of the latter approach.