FCTA 2023 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 24
Title:

Approximations of New MV-Valued Types of Fuzzy Sets
Authors:

Jiří Močkoř
Abstract: Many of the new types of fuzzy sets, such as intuitionistic, neutrosophic, multi-level or fuzzy soft sets and their combinations, can be transformed into one common type of fuzzy sets, called (R,R*)-fuzzy sets, with values in a set R that is a common underlying set of complete commutative idempotent semirings R and R*. For (R,R*)-fuzzy sets, the theory of lower and upper approximations by (R,R*)-relations is defined and the basic properties of these approximations are presented. Using examples of the transformation of some new types of MV-valued fuzzy sets and corresponding fuzzy relations into R-fuzzy sets and R-fuzzy relations, examples of approximation of these new types of fuzzy sets through their fuzzy relations are presented, without having to define these operators separately for each new type of fuzzy set.
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Paper Nr: 42
Title:

Measuring and Ranking Bipolarity via Orthopairs
Authors:

Zoltán E. Csajbók
Abstract: Orthopairs, i.e., disjoint sets, are reasonable means to represent bipolar information. Bipolarity has different models; we use the well-known Dubois-Prade typology. Of course, bipolarity can also carry uncertainty. In this paper, we investigate mainly the bipolarity of type II. In Pawlak’s rough set theory, this bipolarity type, with its uncertainty, can be modeled naturally. The “positive” and “negative” sets form an orthopair whose two sets can be approximated by rough sets separately. Rough sets represented by nested sets can be considered an interval set structure. With the help of counting measure, interval numbers can be assigned to the nested sets. Then, relying on interval arithmetic, taking into account the uncertain nature of bipolarity, the degree of bipolarity can be measured, and the positive and negative sets ranked.
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Paper Nr: 45
Title:

On the Categories of Coalgebras, Dialgebras and Powerset Theory over L-Fuzzy Approximation Spaces
Authors:

Sutapa Mahato and S. P. Tiwari
Abstract: This paper is to establish a relationship between powerset theories and the category of dialgebras over the category of L-fuzzy approximation space, where L is a residuated lattice. Also, we show that the category FAS of L-fuzzy approximation spaces is a category of F-coalgebras. Interestingly, we introduce a functor having both the left/right adjoint from the category FAS to the category UAS of upper approximation sets. Further, the category J-Coal of J-coalgebras and the category (J,K)-Dial of (J;K)-dialgebras are introduced over the category FAS and it is shown that they are topological categories.
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Paper Nr: 47
Title:

Fuzzy Least Squares and Fuzzy Orthogonal Least Squares Linear Regressions
Authors:

Julien Rosset and Laurent Donzé
Abstract: We examine the well known fuzzy least squares linear regression method. We discuss the constrained and unconstrained solutions. Based on the concept of fuzzy orthogonality, we propose the fuzzy orthogonal least squares method to solve fuzzy linear regression problems. We show that, in case of (fuzzy) orthogonal regressors, an important property of the least squares method remains valid. We obtain the same estimates of the parameters of the model if we regress on all regressors, or on each regressor considered separately. An empirical application illustrates our methods.
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Paper Nr: 61
Title:

Semi-Supervised Fuzzy C-Means for Regression
Authors:

Gabriella Casalino, Giovanna Castellano and Corrado Mencar
Abstract: We propose a method to perform regression on partially labeled data, which is based on SSFCM (Semi-Supervised Fuzzy C-Means), an algorithm for semi-supervised classification based on fuzzy clustering. The proposed method, called SSFCM-R, precedes the application of SSFCM with a relabeling module based on target discretization. After the application of SSFCM, regression is carried out according to one out of two possible schemes: (i) the output corresponds to the label of the closest cluster; (ii) the output is a linear combination of the cluster labels weighted by the membership degree of the input. Some experiments on synthetic data are reported to compare both approaches.
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Paper Nr: 73
Title:

Experimental Assessment of Heterogeneous Fuzzy Regression Trees
Authors:

José C. Bárcena, Pietro Ducange, Riccardo Gallo, Francesco Marcelloni, Alessandro Renda and Fabrizio Ruffini
Abstract: Fuzzy Regression Trees (FRTs) are widely acknowledged as highly interpretable ML models, capable of dealing with noise and/or uncertainty thanks to the adoption of fuzziness. The accuracy of FRTs, however, strongly depends on the polynomial function adopted in the leaf nodes. Indeed, their modelling capability increases with the order of the polynomial, even if at the cost of greater complexity and reduced interpretability. In this paper we introduce the concept of Heterogeneous FRT: the order of the polynomial function is selected on each leaf node and can lead either to a zero-order or a first-order approximation. In our experimental assessment, the percentage of the two approximation orders is varied to cover the whole spectrum from pure zero-order to pure first-order FRTs, thus allowing an in-depth analysis of the trade-off between accuracy and interpretability. We present and discuss the results in terms of accuracy and interpretability obtained by the corresponding FRTs on nine benchmark datasets.
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Short Papers
Paper Nr: 57
Title:

A New Approach to Addressing Uncertainty in Information Technology with Fuzzy Multi-Criteria Decision Analysis
Authors:

Elissa N. Madi, Azwa A. Aziz and Binyamin Yusof
Abstract: The problem of reasoning under uncertainty is widely recognised as significant in information technology, and a wide range of methods has been proposed to address this problem. Uncertainty happens when imperfect information is the only available source to solve it using quantitative methods. Therefore, there is a need to implement a qualitative method when no numerical information is available. Linguistic uncertainties related to the qualitative part must be considered and managed wisely. Such uncertainty commonly involves in decision-making problem which depends on human perceptions. This study explores the relationship and difference between two variables, namely the level of uncertainty to the input and the output changes based on multi-criteria decision analysis. There is a positive relationship between these two variables. The novel generation interval type-2 fuzzy membership function technique is proposed based on this. It can accurately map the decision maker’s perceptions to the fuzzy set model, reducing the potential for loss of information. In literature, the output ranking of the system is presented as a crisp number. However, this study proposed a new form of output in interval form based on multi-criteria decision analysis. Overall, this study provides new insight into how we should not ignore uncertainty when it affects the input. It provides an intelligent way to map human perceptions to the system using a fuzzy set.
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Paper Nr: 69
Title:

A Novel Fuzzy Geometric Naive Bayes Network for Online Skills Assessment in Training Based on Virtual Reality
Authors:

Jodavid A. Ferreira, Arthur R. Lopes, Liliane S. Machado and Ronei M. Moraes
Abstract: Computational intelligence-based assessment systems have been proposed for implementation in virtual reality (VR) simulators to enhance technical proficiency in secure environments. Traditional training methods in healthcare, such as live subjects, cadavers, or mannequins, have limitations in reflecting realistic characteristics and deteriorate over time. Virtual reality-based assessment systems offer the advantage of check users skills in realistic and immersive training experiences, providing feedback at the end of the training. This paper presents a novel approach to assessment using a Single-User Assessment System (SUAS) that incorporates a Fuzzy Geometric Naive Bayes Network. The proposed method utilizes geometric distribution to model the fuzzy boundaries and assess the performance of gynecological examinations in a virtual reality simulator. The study evaluates the effectiveness of the proposed SUAS by comparing it with three other assessment methods. The results demonstrate the superior performance of the proposed method in accurately evaluating user performance in the simulated gynecological examinations.
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