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When a combination of convexity and continuity forces monotonicity of preferences Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210610
Hirbod Assa, Alexander ZimperWe consider arbitrary subsets L of random variables defined on an arbitrary nonadditive probability space (Ω,F,ν). A topology τ on L satisfies Condition BU if every open set in this topology which contains X∈L as a member also contains as a subset some (c,ϵ)–ball around X, defined as Bc,ϵ(X)={Y∈Lν(X−Y≥c)<ϵ}. Condition BU is satisfied by any topology of convergence in nonadditive measure ν [21]

Textural dependency and concept lattices Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210605
Sadık BayhanIn this paper, the dependence spaces are discussed for textural formal concepts considering the method given by Ma et al. A complete congruence on a complete lattice is an equivalence relation if it satisfies the infinite substitution property. More generally, a joindependence and a meetdependence space with respect to infinite domain of discourse are presented. Using the duality in textures, the

On the distributivity of fuzzy implications and the weighted Simplications Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210610
József Dombi, Michał BaczyńskiThe distributivity properties of fuzzy implication functions play an important role in fuzzy research. Making use of the solution of the autodistributivity functional equations, we give the necessary and sufficient conditions of two types of the distributivity property of fuzzy implications. It is essentially based on the weighted disjunctive operator, and new weighted and mean (S,N)implication functions

Nvertical generated implications and their distributivities over tnorms and tconorms Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210609
Yafei Cheng, Bin ZhaoOne of the methods to construct a new fuzzy implication from given ones is to transform the given implications and appropriately scale the variables. In this paper, we firstly construct a Nvertical generated implication by changing the linear transformations of two initial implications of the recent introduced vertical ethreshold generated implication. Then we give the relationship between these

Probability over Płonka sums of Boolean algebras: States, metrics and topology Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210526
Stefano Bonzio, Andrea LoiThe paper introduces the notion of state for involutive bisemilattices, a variety which plays the role of algebraic counterpart of weak Kleene logics and whose elements are represented as Płonka sums of Boolean algebras. We investigate the relations between states over an involutive bisemilattice and probability measures over the (Boolean) algebras in the Płonka sum representation and, the direct limit

A decompositionbased algorithm for learning the structure of multivariate regression chain graphs Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210603
Mohammad Ali Javidian, Marco ValtortaWe extend the decomposition approach for learning Bayesian networks (BNs) proposed by Xie et al. (2006) [55] to learning multivariate regression chain graphs (MVR CGs), which include BNs as a special case. The same advantages of this decomposition approach hold in the more general setting: reduced complexity and increased power of computational independence tests. Moreover, latent (hidden) variables

Constructing uninorms on bounded lattices by using additive generators Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210527
Peng He, Xueping WangIn this article, we present two methods to construct uninorms on bounded lattices by using additive generators. We also provide some examples for illustrating the constructing methods of uninorms.

Modal systems for covering semantics and boundary operator Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210519
Vineeta Singh Patel, Md. Aquil Khan, Mihir Kumar ChakrabortyTowards the study of covering based rough set semantics for modal logic [39], we present a modal system corresponding to the covering systems P3,C1, and CGr. We also study the modal systems for boundary operators based on generalized approximation spaces as well as covering systems. Our study also leads to covering semantics for some contingency logics and provide its connection with rough set theory

Adversarial domain adaptation network for tumor image diagnosis Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210508
Chunmei He, Shunmin Wang, Hongyu Kang, Lanqing Zheng, Taifeng Tan, Xianjun FanIn medical fields it is very difficult and timeconsuming to label samples. Only a small number of labeled samples or unlabeled samples are often encountered in medical fields. How to deal with this problem in medical diagnosis? Domain adaptation is an effective machine learning method to solve the scarce or no labeled samples problem. In this paper, an improved adversarial domain adaptation network

Threeway recommendation model based on shadowed set with uncertainty invariance Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210511
Chengying Wu, Qinghua Zhang, Fan Zhao, Yunlong Cheng, Guoyin WangRecommender systems are an effective tool to resolve information overload by enabling the selection of the subsets of items from a universal set based on user preferences. The operation of most of recommender systems depends on the prediction ratings, which may introduce a degree of uncertainty into the process of recommendation. However, systems equipped with only two strategies lack the flexibility

Defining rough sets as core–support pairs of threevalued functions Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210513
Jouni Järvinen, Sándor RadeleczkiWe answer the question what properties a collection F of threevalued functions on a set U must fulfil so that there exists a quasiorder ≤ on U such that the rough sets determined by ≤ coincide with the core–support pairs of the functions in F. Applying this characterization, we give a new representation of rough sets determined by equivalences in terms of threevalued Łukasiewicz algebras of threevalued

Trust recommendation mechanismbased consensus model for Pawlak conflict analysis decision making Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210519
Sirong Tong, Bingzhen Sun, Xiaoli Chu, Xinrui Zhang, Ting Wang, Chao JiangConflict analysis has become a hot issue in management science. In the context of conflict analysis, there are three attitudes for agents to describe the opinion, including supportive, opposite, and neutral. Then, the conflict situation is discussed and analyzed. In this paper, we propose an extended Pawlak conflict model concerning the trust mechanism to solve the problem of the reaching consensus

Characterizing fuzzy simulations for fuzzy labeled transition systems in fuzzy propositional dynamic logic Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210512
Linh Anh NguyenWe formulate and prove logical characterizations of fuzzy simulations for fuzzy labeled transition systems under the Gödel semantics. Apart from the usual setting, we consider also the setting that allows graded modal operators, which are used to talk about the number of successors with a certain property of the current state. Our characterizations are formulated w.r.t. the existential fragment of

Robust moving object detection based on fusing Atanassov's Intuitionistic 3D Fuzzy Histon Roughness Index and texture features Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210503
Davar GivekiBackground modeling is a crucial step in various computer vision applications such as video surveillance, object tracking, and moving object detection. Classifying image pixels as foreground or background is yet a challenging task particularly in complicated situations such as illumination variations, rippling water, camera jitter, and the presence of fast and slow moving objects. Therefore, for a

Residuated implications derived from quasioverlap functions on lattices Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210503
Rui Paiva, Benjamín Bedregal, Regivan Santiago, Thiago VieiraRecently, Paiva et al. generalized the notion of overlap functions in the context of lattices and introduced a weaker definition, called quasioverlap, that originates from the removal of the continuity condition. In this paper, we introduce the concept of residuated implications related to quasioverlap functions on lattices and prove some related properties. We also show that the class of quasioverlap

Construction of nullnorms on some special classes of bounded lattices Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210429
Gül Deniz ÇaylıThis paper continues to study the construction of nullnorms on bounded lattices. We first propose two new construction methods of nullnorms derived from an arbitrary tnorm (resp. an arbitrary tconorm) using a closure operator (resp. an interior operator) on bounded lattices, where some sufficient and necessary conditions are considered. Then, we introduce two other methods to obtain nullnorms based

Information structures in a fuzzy setvalued information system based on granular computing Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210426
Zhaowen Li, Zhihong Wang, Yan Song, ChingFeng WenA fuzzy setvalued information system (FSVIS) refers to an information system (IS) whose information values are fuzzy sets. This article investigates information structures (ISts) in a FSVIS based on granular computing (GrC). First, FSVISs and homomorphism between them are introduced. Next, ISts in a FSVIS are described. The dependence and information distance between ISts are discussed, and characterizations

Modelbased fuzzy time series clustering of conditional higher moments Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210419
Roy Cerqueti, Massimiliano Giacalone, Raffaele MatteraThis paper develops a new time series clustering procedure allowing for heteroskedasticity, nonnormality and model's nonlinearity. At this aim, we follow a fuzzy approach. Specifically, considering a Dynamic Conditional Score (DCS) model, we propose to cluster time series according to their estimated conditional moments via the Autocorrelationbased fuzzy Cmeans (AFCM) algorithm. The DCS parametric

A knowledge based system for the management of a time stamped uncertain observation set with application on preserving mobility Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210417
Véronique Delcroix, Emmanuelle GrislinLe Strugeon, François PuisieuxThe aim of this study is to maintain uptodate information about the current state of elderly people that are medically followed for risks of fall. Our proposal consists of an individual information database management system that can provide information ondemand on various variables. Such a system has to deal with several sources of uncertainty: lack of information, evolving information and reliability

On classifying the effects of policy announcements on volatility Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210419
Giampiero M. Gallo, Demetrio Lacava, Edoardo OtrantoThe financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model–based classification of the dates of a Central Bank's announcements

Fundamental properties of relative entropy and Lin divergence for Choquet integral Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210326
Hamzeh AgahiEntropy is the most important concept used in information theory and measuring uncertainty. In Choquet calculus, Sugeno (2013) [10] and Torra and Narukawa (2016) [2] studied Choquet integral and derivative with respect to monotone measures on the real line. Then as a very challenging problem, the definition of entropy and relative entropy on monotone measures for infinite sets based on Choquet integral

Instance weighting through data imprecisiation Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210415
Julian Lienen, Eyke HüllermeierIn machine learning, instance weighting is commonly used to control the influence of individual data points in a learning process. The general idea is to improve results (e.g., the accuracy of a predictor) by restricting the influence of training examples that do not appear to be representative and may bias the learner in an undesirable way. The simplest and most common approach is to modulate the

A universal approach to imprecise probabilities in possibility theory Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210331
Dominik Hose, Michael HanssPossibility theory is a computationally efficient framework for reasoning with imprecise probabilities. Before performing any possibilistic analysis, however, the (imprecise) probabilistic information about the experiment needs to be expressed in the form of a possibility distribution. In this paper, we propose a novel Imprecise ProbabilitytoPossibility Transformation. This method unifies many results

Fast semisupervised evidential clustering Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210326
Violaine Antoine, Jose A. Guerrero, Jiarui XieSemisupervised clustering is a constrained clustering technique that organizes a collection of unlabeled data into homogeneous subgroups with the help of domain knowledge expressed as constraints. These methods are, most of the time, variants of the popular kmeans clustering algorithm. As such, they are based on a criterion to minimize. Amongst existing semisupervised clusterings, Semisupervised

Improving and benchmarking of algorithms for Γmaximin, Γmaximax and interval dominance Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210324
Nawapon Nakharutai, Matthias C.M. Troffaes, Camila C.S. CaiadoΓmaximin, Γmaximax and interval dominance are familiar decision criteria for making decisions under severe uncertainty, when probability distributions can only be partially identified. One can apply these three criteria by solving sequences of linear programs. In this study, we present new algorithms for these criteria and compare their performance to existing standard algorithms. Specifically, we

Characterization of nuninorms with continuous underlying functions via zordinal sum construction Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210324
Andrea MesiarováZemánkováThe nuninorms with continuous underlying tnorms and tconorms are characterized via the zordinal sum construction. We show that each nuninorm with continuous underlying tnorms and tconorms can be expressed as a zordinal sum of a countable number of Archimedean and idempotent semigroups with respect to the branching set A∼{z1,…,zn−1}, where the corresponding partial order has a tree structure

A unified approach to four important classes of unary operators Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210326
József Dombi, Tamás JónásIn this paper, we study operator dependent modifiers and we interpret the dual pair of modal operators based on an algebraic definition. It is a known fact that the substantiating and weakening modifier operators can be induced by repeating the arguments of conjunctive and disjunctive operators. We provide the conditions for which these modifier operators satisfy the requirements for a dual pair of

Analysis and enhanced prediction of the Spanish Electricity Network through Big Data and Machine Learning techniques Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210316
M.C. Pegalajar, L.G.B. Ruiz, M.P. Cuéllar, R. RuedaElectricity demand is shown to steadily increase in the last few years, and it is one of the key aspects of living standards and quantifying welfare effects. However, the irregularity of electricity demand is one of the main problems in this field. Therefore, it is important to accurately anticipate future expenditures in order to optimize energy generation and to avoid unexpected wastes. As a result

A test to compare interval time series Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210319
Elizabeth Ann Maharaj, Paula Brito, Paulo TelesWe compare two interval time series (ITS) by testing whether their underlying distributions are significantly different or not. To perform hypothesis testing, we make use of the discrete wavelet transform (DWT) which decomposes a time series into a set of coefficients over a number of frequency bands or scales. We obtain the DWT of the radius and centre of each of the two ITS at different scales, and

Onedimensional gametheoretic differential equations Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210324
Rafał M. Łochowski, Nicolas Perkowski, David J. PrömelWe provide a very brief introduction to typical paths and the corresponding Itô type integration. Relying on this robust Itô integration, we prove an existence and uniqueness result for onedimensional differential equations driven by typical paths with nonLipschitz continuous coefficients in the spirit of Yamada–Watanabe as well as an approximation result in the spirit of Doss–Sussmann.

Dynamic panel fuzzy time series model and its application to econometric time series Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210323
Nevin Güler Dincer, Arzu EkiciThis study proposes a new Fuzzy Time Series (FTS) approach, called as Dynamic Panel Fuzzy Time Series (DPFTS) which combines Dynamic Panel Data Analysis and FTS. The major advantages of proposed approach can be summarized as follows: i) proposed approach is adapted version of traditional fuzzy time series (TFTS) model to time series data having more than two crosssections such as countries, cities

Rule extraction based on linguisticvalued intuitionistic fuzzy layered concept lattice Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210301
Li Zou, Hongmei Lin, Xiaoying Song, Kaihua Feng, Xin LiuAs one of the research tools for data processing and knowledge discovery, concept lattice can effectively extract information. In daily life, due to the ambiguity and uncertainty of the decision environment, different experts may provide different evaluation information according to individual needs which may be expressed with linguistic values. In order to handle these problems, we study the rule

A robust support vector regression with exact predictors and fuzzy responses Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210301
M. Asadolahi, M.G. Akbari, G. Hesamian, M. ArefiIn this paper, a new method is proposed for estimating fuzzy regression models based on a novel robust support vector machines with exact predictors and fuzzy responses. For this purpose, a threestage support vector machine algorithm was introduced based on a modified robust loss function. Some common goodnessoffit criteria and a popular kernel were also employed to examine the performance of the

Average behaviour in discretetime imprecise Markov chains: A study of weak ergodicity Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210308
Natan T'Joens, Jasper De BockWe study the limit behaviour of upper and lower bounds on expected time averages in imprecise Markov chains; a generalised type of Markov chain where the local dynamics, traditionally characterised by transition probabilities, are now represented by sets of ‘plausible’ transition probabilities. Our first main result is a necessary and sufficient condition under which these upper and lower bounds, called

Logics of imprecise comparative probability Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210218
Yifeng Ding, Wesley H. Holliday, Thomas F. IcardThis paper studies connections between two alternatives to the standard probability calculus for representing and reasoning about uncertainty: imprecise probability and comparative probability. The goal is to identify complete logics for reasoning about uncertainty in a comparative probabilistic language whose semantics is given in terms of imprecise probability. Comparative probability operators are

Graded polygons of opposition in fuzzy formal concept analysis Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210302
Stefania Boffa, Petra Murinová, Vilém NovákQuantifierbased operators are fuzzy quantifiers that are, similarly to the intermediate quantifiers, based on the evaluative linguistic expressions not small, very big and extremely big. This article focuses mainly on achieving two goals. Firstly, quantifierbased operators are introduced to create extended fuzzy concept lattices that capture more detailed information from datasets compared to the

Feature selection and threshold method based on fuzzy joint mutual information Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210223
Omar A.M. Salem, Feng Liu, YiPing Phoebe Chen, Xi ChenImproving classification performance is one of the main challenges in a variety of realworld applications. Unfortunately, classification models are sensitive to undesirable features of data such as redundant and irrelevant features. Feature selection (FS) is a powerful solution to address the negative effect of these features. Among various methods, Feature selection based on mutual information (MI)

Conflict analysis based on threeway decision for triangular fuzzy information systems Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201219
Xiaonan Li, Xuan Wang, Guangming Lang, Huangjian YiTriangular fuzzy numbers (TFNs) can not only provide the range of fuzzy points, but contain the three most representative fuzzy points, which play an essential role in describing fuzzy information. Combining with conflict situations, the agents' caution in the decisionmaking process can be well depicted by TFNs. However, little effort has been paid to conflict analysis on triangular fuzzy information

On (IO,O)fuzzy rough sets based on overlap functions Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210208
Junsheng QiaoIn the last past years, as a class of continuous binary aggregation functions, there are many scholars keeping a watchful focus on overlap functions for their widely applicability in various actual problems. On the other side, after that rough sets were presented as a formal tool to handle indetermination and inaccuracy in the analysis of data, there arise many works concerning the extension study

New measures of alliance and conflict for threeway conflict analysis Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210210
Guangming Lang, Yiyu YaoIn the Pawlak model of conflict analysis, a rating of −1, 0, and +1 indicates that an agent is negative, neutral, and positive towards an issue. One defines measures of alliance and conflict of agents based on their threevalued ratings on a set of issues. Existing studies make some restrictive assumptions. One assumption is that a single distance function determines alliance, conflict, and neutrality

Classifying and completing word analogies by machine learning Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210208
Suryani Lim, Henri Prade, Gilles RichardAnalogical proportions are statements of the form ‘a is to b as c is to d’, formally denoted a:b::c:d. They are the basis of analogical reasoning which is often considered as an essential ingredient of human intelligence. For this reason, recognizing analogies in natural language has long been a research focus within the Natural Language Processing (NLP) community. With the emergence of word embedding

Distributivity of Nordinal sum fuzzy implications over tnorms and tconorms Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210204
Qing Chang, Hongjun ZhouDistributivity of fuzzy implications over tnorms and tconorms provides an effective way to solve the combinatorial rule explosion problems caused by addition of inputs in fuzzy inference systems, and hence has received considerable attention in the literature. In this paper, we explore the distributivity of a newlyborn class of ordinal sum fuzzy implications with respect to tnorms and tconorms

Revealed preference in argumentation: Algorithms and applications Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210202
Nguyen Duy Hung, VanNam HuynhArgumentative agents in AI are inspired by how humans reason by exchange of arguments. Given the same set of arguments possibly attacking one another (Dung's AA framework) these agents are bound to accept the same subset of those arguments (aka extension) unless they reason by different argumentation semantics. However humans may not be so predictable, and in this paper we assume that this is because

Largescale empirical validation of Bayesian Network structure learning algorithms with noisy data Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210125
Anthony C. Constantinou, Yang Liu, Kiattikun Chobtham, Zhigao Guo, Neville K. KitsonNumerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case for the algorithm proposed in that publication and results across studies are often inconsistent in their claims about which algorithm is ‘best’. This is partly because there is no agreed evaluation approach to determine

On standard completeness and finite model property for a probabilistic logic on Łukasiewicz events Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210108
Tommaso FlaminioThe probabilistic logic FP(Ł,Ł) was axiomatized with the aim of presenting a formal setting for reasoning about the probability of infinitevalued Łukasiewicz events. Besides several attempts, proving that axiomatic system to be complete with respect to a class of standard models, remained an open problem since the first paper on FP(Ł,Ł) was published in 2007. In this article we give a solution to

The measurement of relations on belief functions based on the Kantorovich problem and the Wasserstein metric Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210121
Andrey G. Bronevich, Igor N. RozenbergIn this paper, we show how the Kantorovich problem appears in many constructions in the theory of belief functions. We demonstrate this on several relations on belief functions such as inclusion, equality and intersection of belief functions. Using the Kantorovich problem we try to measure these relations and as the result we obtain various functionals like the Wasserstein distance on belief functions

A semisupervised deep learning image caption model based on Pseudo Label and Ngram Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201228
Cheng Cheng, Chunping Li, Youfang Han, Yan ZhuImage caption is an important application field of artificial intelligence technique. When a machine can describe a picture reasonably like a human, it represents that the machine has higher intelligence to understand the picture. However, for complex machine learning tasks such as image caption, data annotation is timeconsuming and laborious. Usually in a new application scenario, data annotation

Gaussian fuzzy theoretic analysis for variational learning of nested compositions Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210104
Mohit Kumar, Sukhvir Singh, Bernhard FreudenthalerThis paper introduces a variational analysis approach to the learning of a deep model formed via a nested composition of mappings. The fuzzy sets, being characterized by Gaussian type of membership functions, are used to represent unknown functions associated to the layers of the model. The learning of the deep model would require a quantification of the uncertainties on the signals across the layers

A particular upper expectation as global belief model for discretetime finitestate uncertain processes Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201229
Natan T'Joens, Jasper De Bock, Gert de CoomanTo model discretetime finitestate uncertain processes, we argue for the use of a global belief model in the form of an upper expectation that is the most conservative one under a set of basic axioms. Our motivation for these axioms, which describe how local and global belief models should be related, is based on two possible interpretations for an upper expectation: a behavioural one similar to Walley's

Graphoid properties of concepts of independence for sets of probabilities Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201228
Fabio Gagliardi CozmanWe examine several concepts of independence associated with (1) credal sets, understood as sets of probability measures, (2) sets of full conditional probabilities, (3) sets of lexicographic probabilities, and (4) sets of desirable gambles. Concepts of independence are evaluated with respect to the graphoid properties they satisfy, as these properties capture important abstract features of “independence”

Multiple multidimensional linguistic reasoning algorithm based on propertyoriented linguistic concept lattice Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201218
Hui Cui, Guanli Yue, Li Zou, Xin Liu, Ansheng DengAiming at the difficult problems of dealing with mass linguistic information in uncertain environment, this paper mainly focuses on a linguistic reasoning algorithm based on propertyoriented linguistic concept lattice by combining concept lattice and neural network. Specifically, we present a propertyoriented linguistic concept lattice to express linguistic information between concepts based on linguistic

A note on the relationships between generalized rough sets and topologies Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201228
Qiu Jin, Lingqiang Li, Zhenming Ma, Bingxue YaoQuite recently, Wu and Liu (2020) [11] raised an open problem when they discussed the relationships between generalized rough sets and topologies. Said precisely, each binary relation generates a topology through the lower rough approximation operator, then for two binary relations on the same set, is there a sufficient and necessary condition such that the union of generated topologies by two binary

Semantic classification of qualitative conditionals and calculating closures of nonmonotonic inference relations Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201229
Steven Kutsch, Christoph BeierleQualitative conditionals of the form “If A, then usually B” are often used to model nonmonotonic inference relations. Evaluating conditionals as three valued logical objects, allows for a classification of all conditionals over a given propositional signature. These classes of conditionals and their properties in terms of nonmonotonic inference are useful for the task of calculating the closures of

AH3: An adaptive hierarchical feature representation model for threeway decision boundary processing Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201221
Jie Chen, Yang Xu, Shu Zhao, Yanping ZhangThreeway decision theory is an effective method to deal with uncertain data in classification problems. For binary classification, it divides samples into positive, negative and boundary regions (POS, NEG, and BND). The BND region is regarded as a feasible selection of decisionmaking when the useful information is too limited to make a correct decision, which needs further processing to improve the

Belief rule mining using the evidential reasoning rule for medical diagnosis Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201218
Leilei Chang, Chao Fu, Wei Zhu, Weiyong LiuA belief rule mining approach is proposed to generate belief rules with a customized set of criteria by mining from multiple belief rules that are trained using data with varied sets of criteria. As the theoretical basis of the belief rule mining approach, the key concepts are defined, including the weights and reliabilities of cases, criteria, models, and belief rules. Based on the key concepts, multiple

Detection of rare events with uncertain outcomes Int. J. Approx. Reason. (IF 2.678) Pub Date : 20210104
Roman IlinChoquet Expected Utility framework for decision making under uncertainty is compared with the Expected Utility framework in a scenario involving rare events with potentially catastrophic consequences. The scenario involves detection of suspected criminal activity in a surveillance system. The results of simulation studies show the advantages of using the Choquet framework to model pessimistic attitude

On nonlinear expectations and Markov chains under model uncertainty Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201224
Max NendelThe aim of this work is to give an overview on nonlinear expectations and to relate them to other concepts that describe model uncertainty or imprecision in a probabilistic framework. We discuss imprecise versions of stochastic processes with a particular interest in imprecise Markov chains. First, we focus on basic properties and representations of nonlinear expectations with additional structural

On the crossmigrativity of uninorms revisited Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201223
WenHuang Li, Feng QinCrossmigrative equation between aggregation operators (for example, tnorms) is a weaker form of the classical commuting equation. The work is dedicated to the study of crossmigrativity involving uninorms with continuous underlying operators. The investigation is presented in two separate parts: the first part focuses on the case where one of the uninorms belongs either to the set Umin or Umax. The

A Bayesian method for calibration and aggregation of expert judgement Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201211
David Hartley, Simon FrenchThis paper outlines a Bayesian framework for structured expert judgement (sej) that can be utilised as an alternative to the traditional nonBayesian methods (including the commonly used Cooke's Classical model [13]). We provide an overview of the structure of an expert judgement study and outline opinion pooling techniques noting the benefits and limitations of these approaches. Some new tractable

Rough set reasoning using answer set programs Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201210
Patrick Doherty, Andrzej SzalasReasoning about uncertainty is one of the main cornerstones of Knowledge Representation. Formal representations of uncertainty are numerous and highly varied due to different types of uncertainty intended to be modeled such as vagueness, imprecision and incompleteness. There is a rich body of theoretical results that has been generated for many of these approaches. It is often the case though, that