FCTA 2019 Abstracts


Full Papers
Paper Nr: 1
Title:

Risk-sensitive Markov Decision Processes with Risk Constraints of Coherent Risk Measures in Fuzzy and Stochastic Environment

Authors:

Yuji Yoshida

Abstract: Risk-sensitive decision making with constraints of coherent risk measures is discussed in Markov decision processes. Risk-sensitive expected rewards under utility functions are approximated by weighted average value-at-risks, and risk constraints are described by coherent risk measures. In this paper, coherent risk measures are represented as weighted average value-at-risks with the best risk spectrum derived from decision maker’s risk averse utility, and the risk spectrum can inherit the risk averse property of the decision maker’s utility as weighting. By perception-based extension for fuzzy random variables, a dynamic portfolio model with coherent risk measures is introduced. To find feasible regions, firstly a dynamic risk-minimizing problem is discussed by mathematical programming. Next a risk-sensitive reward maximization problem under the feasible coherent risk constraints is demonstrated. A few numerical examples are given to understand the obtained results.

Paper Nr: 6
Title:

Correlation Coefficient of Modal Level Operators: An Application to Medical Diagnosis

Authors:

Alex Bertei, Renata S. Reiser and Luciana Foss

Abstract: This paper studies the correlation coefficient (A-CC) related to the Atanassov’s intuitionistic fuzzy sets (A-IFS) which are obtained as image of modal operators. Extended results from the action of A-CC over necessity and possibility modal operators are considered, determining the A-CC of A-IFS obtained as image of the !A and ?A modal level operators and discussing the main conditions under which the main properties related to such fuzzy sets are preserved by conjugate and complement operations. In addition, a simulation based on the proposal methodology using modal level operators is applied to a medical diagnosis analysis.

Paper Nr: 9
Title:

Interpreting Xor Intuitionistic Fuzzy Connectives from Quantum Fuzzy Computing

Authors:

Anderson Avila, Renata Reiser, Maurício Pilla and Adenauer Yamin

Abstract: Computer systems based on intuitionistic fuzzy logic are capable of generating a reliable output even when handling inaccurate input data by applying a rule based system, even with rules that are generated with imprecision. The main contribution of this paper is to show that quantum computing can be used to extend the class of intuitionistic fuzzy sets with respect to representing intuitionistic fuzzy Xor operators. This paper describes a multi-dimensional quantum register using aggregations operators such as t-(co)norms based on quantum gates allowing the modeling and interpretation of intuitionistic fuzzy Xor operations.

Paper Nr: 11
Title:

Uncertainty and Fuzzy Modeling in Human-robot Navigation

Authors:

Rainer Palm and Achim J. Lilienthal

Abstract: The interaction between humans and mobile robots in shared areas requires a high level of safety especially at the crossings of the trajectories of humans and robots. We discuss the intersection calculation and its fuzzy version in the context of human-robot navigation with respect to noise information. Based on known parameters of the Gaussian input distributions at the orientations of human and robot the parameters of the output distributions at the intersection are to be found by analytical and fuzzy calculation. Furthermore the inverse task is discussed where the parameters of the output distributions are given and the parameters of the input distributions are searched. For larger standard deviations of the orientation signals we suggest mixed Gaussian models as approximation of nonlinear distributions.

Short Papers
Paper Nr: 5
Title:

A Synergistic Approach to Enhance the Accuracy-interpretability Trade-off of the NECLASS Classifier for Skewed Data Distribution

Authors:

Jamileh Yousefi, Andrew Hamilton-Wright and Charlie Obimbo

Abstract: NEFCLASS is a common example of a neuro-fuzzy system. The popular NEFCLASS classifier exhibits surprising behaviour when the feature values of the training and testing datasets exhibit significant skew. This paper presents a combined approach to improve the classification accuracy and interpretability of the NEFCLASS classifier, when data distribution exhibits positive skewness. The proposed model consists of two steps. Firstly, we used an alternative discretization method to initialize fuzzy sets. Secondly, we devised a statistical rule pruning algorithm based on adjusted residual to reduce the number of rules, thus improving interpretability. This method improves the interpretability of NEFCLASS without significant accuracy deterioration. Moreover, a hybrid approach combining the two approaches is developed to increase the accuracy-interpretability trade-off of NEFCLASS.

Paper Nr: 7
Title:

Fuzzy–rough Fuzzification in General FL Classifiers

Authors:

Janusz T. Starczewski, Robert K. Nowicki and Katarzyna Nieszporek

Abstract: In this paper, a three-dimensional version of fuzzy-rough fuzzification is examined for classification tasks. Similar approach based on interval fuzzy-rough fuzzification has been demonstrated to classify with three decision labels of confidence, one of which were uncertain. The method proposed here relies on the use of fuzzification of inputs with a triangular membership function describing the nature of imprecision in data. As a result, we implement in fuzzy classifiers three dimensional membership functions using the calculus of general type-2 fuzzy sets. The approach is justified when more confidence labels are expected from the decision system, especially when the classifier is embedded in a recurrent hierarchical decision system working on easily available economic, extended, and advanced expensive real data.

Paper Nr: 8
Title:

Extended Possibilistic Fuzzification for Classification

Authors:

Robert K. Nowicki, Janusz T. Starczewski and Rafał Grycuk

Abstract: In this paper, the extended possibilistic fuzzification for classification is proposed. Similar approach with the use of fuzzy–rough fuzzification (Nowicki and Starczewski, 2017; Nowicki, 2019) allows to obtain one of three decisions, i.e. ”yes”, ”no”, and ”I do not know”, The last label occurs when input information is imprecise, incomplete or in general uncertain, and consequently, determining the unequivocal decision is impossible. We extend three-way decision (Hu et al., 2017; Liu et al., 2016; Sun et al., 2017; Yao, 2010; Yao, 2011) into four-way decision by extending possibilistic fuzzification to the three–dimensional possibility and necessity measures of fuzzy events.

Paper Nr: 12
Title:

The Real Transform: Computing Positive Solutions of Fuzzy Polynomial Systems

Authors:

Philippe Aubry, Jérémy Marrez and Annick Valibouze

Abstract: This paper presents an efficient method for finding the positive solutions of polynomial systems whose coefficients are symmetrical L-R fuzzy numbers with bounded support and the same bijective spread functions. The positive solutions of a given fuzzy system are deduced from the ones of another polynomial system with real coefficients, called the real transform. This method is based on new results that are universal because they are independent from the spread functions. We propose the real transform T (E) of a fuzzy equation (E), which positive solutions are the same as those of (E). Then we compare our approach with the existing method of the crisp form system.

Paper Nr: 13
Title:

Evolutionary Fuzzy Logic-based Model Design in Predicting Coronary Heart Disease and Its Progression

Authors:

Christina Brester, Vladimir Stanovov, Ari Voutilainen, Tomi-Pekka Tuomainen, Eugene Semenkin and Mikko Kolehmainen

Abstract: Various data-driven models are often involved in epidemiological studies, wherein the availability of data is constantly increasing. Accurate and, at the same time, interpretable models are preferable from the practical point of view. Finding simple and compact dependences between predictors and outcome variables makes it easier to understand necessary interventions and preventive measures. In this study, we applied a Fuzzy Logic-based model, which meets these requirements, to predict the coronary heart disease (CHD) progression during a 30-year follow-up. The Fuzzy Logic-based model was automatically designed with an ad hoc Genetic Algorithm using the data from the Kuopio Ischemic Heart Disease Risk Factor (KIHD) Study, a Finnish cohort of 2682 men who were middle-aged at baseline in 1980s. Using cross-validation, we found out that the sample from the KIHD study is heterogeneous and after filtering out 10% of outliers, the predictive accuracy increased from 65% to 73%. The generated rule bases include 19 fuzzy rules on average with maximum 7 variables in one rule from the initial set of 638 predictor variables. The selected predictors of CHD progression are informative and diverse representing physical aspects, behavior, and socioeconomics. The Fuzzy Logic-based model creates a comprehensive set of predictors that enables us to better understand the complexity of illnesses and their progression. Moreover, the Fuzzy Logic-based model has potential to provide tools to analyse and deal with heterogeneity in large cohorts.

Paper Nr: 14
Title:

A Fuzzy Logic Programming Environment for Recycling Facility Selection

Authors:

Esra Çakir and H. Z. Ulukan

Abstract: Recycling of wastes is a crucial subject for a sustainable environment. One of the main problem in this area is the appropriate location of the collection centers and recycling facilities. These facilities can be paired according to the criteria such as: distance, cost, type of waste. In this paper, fuzzy linguistics Prolog is used to find importance weights of selection criteria and to match facilities for decision making process. Bousi~Prolog is a fuzzy Prolog that enables working with both fuzzy linguistic and linguistic tools to guide the Prolog systems towards computing with paradigm phrases that can be very helpful to the linguistic resources.

Paper Nr: 2
Title:

A Novel Method for Evaluating Records from a Dataset using Interval Type-2 Fuzzy Sets

Authors:

Miljan Vučetić and Aleksej Makarov

Abstract: In this paper, we describe a method for evaluating suitable records from heterogeneous datasets based on interval type-2 fuzzy sets (IT2FSs). Retrieving records from a dataset including numerical, categorical, binary and fuzzy data in accordance with diverse user’s preferences is still a challenging task. The main challenge is how to deal with heterogeneity present when data in attribute values and user’s preferences are different by nature, e.g. when users explain their interests in linguistic term(s), whereas the attribute value is stored as a number and vice versa. Furthermore, a user may have different interests among desired preferences expressed with different data types. Using fuzzy theory can effectively help in handling heterogeneity in building robust query engines. This efficacy is mitigated when two or more values belong to an ordinary (type-1) fuzzy set with the same membership degree. We propose a solution based on IT2FSs, which are capable to better represent uncertainty in data and preferences. It efficiently improves the ranking of suitable records retrieved from datasets. The connection with aggregation of interval-valued data is also discussed.

Paper Nr: 3
Title:

Fuzzy-Based Recommendation System for University Major Selection

Authors:

Shaima Alghamdi, Nada Alzhrani and Haneen Algethami

Abstract: The decision of choosing a university major is one of the most important decisions in every adult life. To make a suitable decision, a student needs an expert opinion, time, and effort. Therefore, a decision-making system should be developed in order to help prospective students to increase their educational outcome and productivity. In Saudi Arabia, each university requires specific criteria in order to accept students. These criteria are made based on two factors: 1) the outcome of student’s qualification exams and 2) overall high school grades. The student must take these calculations into consideration when selecting a major. Thus, in this paper, a Fuzzy-Based Recommendation System (FRS) is proposed to aid students in choosing a suitable major. This system designed using Fuzzy Expert System (FES). Additionally, a cluster-based preferences technique is implemented to obtain the student's preferred majors, using distance measurement. The system has been tested on fifteen prospective students to measure its accessibility. Results showed that students are stratified by the suggested majors that fell in line with their preferences.