Search

An appropriate visualization of multiobjective nondominated solutions is a valuable asset for decision making. Although there are methods for visualizing the solutions in the design space, they do not provide any information about their relationship. In this paper, we propose a novel methodology that allows the visualization of the nondominated solutions in the design space and their relationships by means of a network. The nodes represent the solutions in the objective space while the edges show the relationships among the solutions in the design space. Our proposal (called moGrams) thus provides a joint visualization of both objective and design spaces. It aims at helping the decision maker to get more understanding of the problem so that (s)he can choose the most appropriate and flexible final solution. moGrams can be applied to any multicriteria problem in which the solutions are related by a similarity metric. Besides, the decision maker interaction is facilitated by modifying the network based on the current preferences to obtain a clearer view. An exhaustive experimental study is performed using four multiobjective problems with a variable number of objectives to show both usefulness and versatility of moGrams. The results exhibit interesting characteristics of our methodology for visualizing and analyzing solutions of multiobjective problems.

Since Zadeh's proposal and Mamdani's seminal ideas, interpretability is acknowledged as one of the most appreciated and valuable characteristics of fuzzy system identification methodologies. It represents the ability of fuzzy systems to formalize the behavior of a real system in a human understandable way, by means of a set of linguistic variables and rules with a high semantic expressivity close to natural language. Interpretability analysis involves two main points of view: readability of the knowledge base description (regarding complexity of fuzzy partitions and rules) and comprehensibility of the fuzzy system (regarding implicit and explicit semantics embedded in fuzzy partitions and rules, as well as the fuzzy reasoning method). Readability has been thoroughly treated by many authors who have proposed several criteria and metrics. Unfortunately, comprehensibility has usually been neglected because it involves some cognitive aspects related to human reasoning, which are very hard to formalize and to deal with. This paper proposes the creation of a new paradigm for fuzzy system comprehensibility analysis based on fuzzy systems' inference maps, so-called fuzzy inference-grams (fingrams), by analogy with scientograms used for visualizing the structure of science. Fingrams show graphically the interaction between rules at the inference level in terms of co-fired rules, i.e., rules fired at the same time by a given input. The analysis of fingrams offers many possibilities: measuring the comprehensibility of fuzzy systems, detecting redundancies and/or inconsistencies among fuzzy rules, identifying the most significant rules, etc. Some of these capabilities are explored in this study for the case of fuzzy models and classifiers.

Understand the behavior of Fuzzy Rule-based Systems (FRBSs) at inference level is a complex task that allows the designer to produce simpler and powerful systems. The fuzzy inference-grams –known as fingrams– establish a novel and mighty tool for understanding the structure and behavior of fuzzy systems. Fingrams represent FRBSs as social networks made of nodes representing fuzzy rules and edges representing the degree of interaction between pairs of rules at inference level (no edge means no significant interaction). We can analyze fingrams obtaining helpful information such as detecting potential conflicts between rules, unused rules and redundant ones. This paper introduces a new module for fingram generation and analysis included in the free software tool GUAJE. This tool aims to design, analyze and evaluate fuzzy systems with good interpretability-accuracy trade-off. In addition, GUAJE includes several intuitive and interactive tutorials to uncover the possibilities it offers. One of them generates and enhances a fuzzy system, analyzing each improvement through the use of fingrams, and lets the user reproduce the illustrative case study described in this paper.

Opinion surveys are widely admitted as a valuable source of information which becomes complementary to the information extracted from data by machine learning techniques. This paper focuses on a challenging and still open problem which is related to how to handle properly the inherent uncertainty of human perceptions. Namely, we propose new ways to interpret and analyze fuzzy data coming out from a special case of survey, the so-called fuzzy rating scale-based questionnaire. This kind of questionnaire is characterized by allowing expressing human perceptions in terms of fuzzy rating scales. The proposed methods are in charge of capturing and modeling the uncertainty of the answers by varying the heights of the related fuzzy sets. These methods have been validated in two case studies: (1) a descriptive survey related to the packaging design of gin bottles; and (2) a comparative survey related to 2015 IFSA-EUSFLAT conference.

This paper presents a software aimed at making easier the design of customized products in accor- dance with consumers' expectations. It is based on a user-centered design methodology which combines techniques provided by Kansei Engineering, Sen- sory Analysis, and Soft Computing. Consumers' expectations are naturally expressed in the form of vague assessments related to human perceptions. Soft Computing techniques, namely Fuzzy Logic, can assist both Sensory Analysis and Kansei Engi- neering in the design and customization of products because of: (1) their suitability for computing with perceptions, and (2) their ability to tackle with im- precision and uncertainty in system identification. The utility of the software has been illustrated with a case study related to gin packaging.

Indoor localization is increasingly required for applications like deployment of rescue teams in emergency situations, proactive care for the elders, and so on. The quick growing of coverage of WiFi networks makes WiFi technology a very promising choice for indoor localization. But, this localization should be linked to a map to be useful. This work presents an open-access software designed for that purpose. It is composed of two different applications, a desktop software for research purposes and an Android application for user friendly localization. We address the localization task as a high dimensional classification problem. So far, we have developed classifiers based on the classic Nearest Neighbour, Support Vector Machines (SVM) and fuzzy rule-based classifiers. This work is made in the context of the ABSYNTHE project which is aimed at creating human-robot teams. We show a use case of the new software in one of the scenarios of the ABSYNTHE project.

The contribution of this paper is to test different models that correctly interpret descriptions of shapes provided by web-users and are able to select which object is being described by them in a language game, a game in which one user describes a selected object of those composing the scene (see figure 1), while another user has to guess which object has been described (see figure 2). The given description needs to be non ambiguous and accurate enough to allow other users to guess the described shape correctly. The descriptions need to be parsed to extract the syntax and the words' classes used, and to interpret the semantics of them. We have modeled the meaning of these descriptions by means of soft constraints, which represent the meaning of words, and an aggregation function which combine the meanings of the words into the meaning of the description. We have proven that if we use the syntax to detect whether a description is simple or complex (those referring to two or more objects) the system can learn better semantic models and improve the results, increasing the precision from 79% to 81%, the recall from 34% to 58% and the f-measure from 48% to 68%.

This paper shows the use of Fingrams ‐Fuzzy Inference-grams‐ aimed at unveiling graphically some hidden details in the usual behavior of the precise fuzzy modeling algorithm FURIA ‐Fuzzy Unordered Rule Induction Algorithm‐. FURIA is recognized as one of the most outstanding fuzzy rule-based classification methods attending to accuracy. Although FURIA usually produces compact rule bases, with low number of rules and antecedents per rule, its interpretability is arguable, being penalized by the absence of linguistic readability and a complex inference mechanism. Fingrams oer a methodology for visual representation and exploratory analysis of fuzzy rule-based systems. FURIA-Fingrams, i.e. fuzzy inference-grams representing fuzzy systems learnt with FURIA, make easier understanding the FURIA inference mechanism thanks to the possibilities they oer