Session Program

Abstract - During the modeling of uncertain and imprecise systems, the intelligent approach, based on the use of so-called Fuzzy Cognitive Maps (FCM) is often used. Constructors of the FCM models usually use a technique, in which fuzzy quantities are converted to their crisp equivalents (e.g. in a model learning phase). Such a procedure converts the fuzzy model in a crisp model, which may be a problem in systems with uncertainty. This risk can be avoided by building a model based on fuzzy numbers, fuzzy relations and fuzzy arithmetic operations, but then the new problem - of technical nature - arises, related to the specifics of operations on fuzzy numbers - manifested in the support deformations. The paper proposes a solution to this problem, consisting in a new look at the interpretation of the results of arithmetic operations on fuzzy numbers. New mechanism, that allows overcoming the negative effects of such deformations, is presented. The use of the proposed approach enables maintaining the fuzzy nature of the model at each stage of its operation. The results of simulations for different variants of the proposed method are also shown.

Abstract - Given high uncertainty in medicine, it is essential to use some approaches to deal with this uncertainty. Therefore, this article utilizes computing with words (CWW) in fuzzy cognitive maps (FCMs) for the application of a new medical decision support system. In this framework, expressed as CWW FCM, all concepts and the weights of connecting links between them are described based on interval type-2 membership functions (IT2 MFs) expressed as a set of words. This type of FCM structure shows high performance by taking into account uncertainties in experts' opinions and in system parameters in the process of modeling. In this paper, we utilize CWW FCM to classify celiac disease (CD), a chronic disorder. Thus, to illustrate the behavior and performance of the proposed model in classifying CD, we have collected a real dataset from patients with different types of CD. Applying the model on this dataset, acceptable accuracy in classification is achieved.

Abstract - This research study proposes a new method for automatic design of Fuzzy Cognitive Maps (FCM) using ordinal data based on the efficient capabilities of mixed graphical models. The approach is able to model all variables on the proper domain of ordinal data by combining a new class of Mixed Graphical Models (MGMs) with a structure estimation approach based on generalized covariance matrices. It can work with a large amount of categorical data. It represents its structure as a sparser graph, while maintaining a high likelihood, by producing an adjacent weight matrix, where relationships are expressed by conditional independences. By maximizing the likelihood indicates that the model fits better to the data under the assumption that the observed data are the most likely data. The whole approach was implemented in a business intelligence problem of evaluating the attractiveness of Belgian companies. Through the analysis of results and conducted scenarios, the usefulness of the proposed MGM method for designing FCM capable to make decisions, is demonstrated. Comparisons with the previous known methodology for automatic construction of FCMs based on distance-based algorithm, showed that the proposed approach provides more understandable/useful relationships among nodes, through a less complex structure for making decisions.

Abstract - The exponentially rising amounts of urban data demand new conceptual and technical methods for their management and storage. The era of the Semantic Web requires a convergence toward commonly shared meanings. The combination of fuzzy cognitive maps with graph databases is a first approach to a solution. This article determines the basic requirements for the storing of fuzzy cognitive maps to test current graph databases for their structural suitability. Six out of 47 graph databases fulfill the requirements and are thus recommended for further research purposes. As a proof of concept, OrientDB is used to present how a semantic convergence can be reached through the combination of fuzzy cognitive maps and graph databases in a cognitive city by tackling fuzziness.

Abstract - Fuzzy Cognitive Maps (FCMs) are a soft computing technique characterized by robust properties that make them an effective technique for medical decision support systems. Making decisions within a medical domain is difficult due to the existence of high levels of uncertainty. The sources of this uncertainty can be due to the variation of physicians' opinions and experiences. The structure of existing FCMs is based on type -1 fuzzy sets in order to represent the causal relations among concepts of the modeled system. Therefore, the ability of the FCM to handle high levels of uncertainties and deliver accurate results can be hindered. In this paper, we propose using the Interval Agreement Approach to model the weights of links in FCMs to capture high level uncertainties in the presence of imprecise data acquired from different medical experts to enhance its decision modelling and reasoning capability. The proposed model is used in identifying if a child is diagnosed with an Autism Spectrum Disorder (ASD) where the Modified Checklist for Autism in Toddlers is used as a standard tool to derive the inputs for the FCMs. Initial results demonstrate that the proposed method outperforms conventional FCMs in classifying ASD based on a dataset of diagnosed cases.

Abstract - This paper presents a novel approach to improve the accuracy of classification models used for prediction purposes by integrating a Fuzzy Cognitive Map (FCM) to produce a hybrid model. The proposed methodology first uses the FCM to discover latent correlations that exist between the data in order to form a single variable. This variable is then fed in the classification model as part of the training and testing phases to enhance its accuracy. Experimental results using datasets describing two different problems suggested noteworthy improvements in the accuracy of various classification models.