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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

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)

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

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

Multivariate statistical matching using graphical modeling Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201210
Pier Luigi Conti; Daniela Marella; Paola Vicard; Vincenzina VitaleThe goal of statistical matching, at a macro level, is the estimation of the joint distribution of variables separately observed in independent samples. The lack of joint information on the variables of interest leads to uncertainty about the data generating model. In this paper we propose the use of graphical models to deal with the statistical matching uncertainty for multivariate categorical variables

Doublequantitative rough sets, optimal scale selection and reduction in multiscale dominance IF decision tables Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201210
Bing Huang; Huaxiong Li; Guofu Feng; Chunxiang Guo; Dafeng ChenIn doublequantitative rough set (DqRS) theory, all kinds of rough set models and attribute reduction methods have recently been examined, and all of them were to acquire knowledge from a particular decision table. However, little attention has been paid to the construction of dominancebased DqRS (DDqRS) models, optimal scale selection and optimal scale reduction in multiscale dominance intuitionistic

A spatial filtering inspired threeway clustering approach with application to outlier detection Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201210
Bahar Ali; Nouman Azam; Anwar Shah; JingTao YaoThreeway clustering provides an effective framework for clustering of data in the presence of uncertain, imprecise and incomplete data. In this article, we used ideas inspired from two commonly used spatial filters from image processing called minimum and maximum filters to construct a threeway clustering approach named RE3WC and explore its application in outlier detection. A threeway cluster is

Some further results about uninorms on bounded lattices Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201210
Bin Zhao; Tao WuThe main purpose of this paper is to solve the problem proposed by Çaylı about uninorms on bounded lattices and build close relationships among uninorms constructed in this paper. Based on the known construction methods and researchers' work, we obtain new uninorms on L with the given tnorm and tconorm by using closure (interior) operators. The new construction methods provide answers to the problem

Limited approximate bisimulations and the corresponding rough approximations Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201209
Sha Qiao; Ping ZhuTo measure the similarity of nodes in the neighboring subgraphs, Milner introduced the notion of klimited bisimilarity. Recently, as a weaker version of klimited bisimilarity, the notion of klimited similarity was proposed and applied to graph pattern matching. (Bi)simulations have been widely used in comparing the behavior of fuzzy transition systems. In order to study the (bi)simulation semantics

Semimonolayer covering rough set on setvalued information systems and its efficient computation Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201210
Zhengjiang Wu; Hui Wang; Ning Chen; Junwei LuoFor setvalued information systems, there are many original dotbased approximation models based on tolerance relations and other developed tolerance relations. Because of the lack of efficient algorithms, they are not to accommodate the bigger and bigger setvalued information table. Therefore, it is a real challenge on how to efficiently calculate a highquality approximation set in setvalued information

Variableprecision threeway concepts in Lcontexts Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201204
Xuerong Zhao; Duoqian Miao; Hamido FujitaThe notion of fuzzy concept is proposed to deal with objectattribute data with Lvalues (where L is a truthvalue structure). One disadvantage of fuzzy concept is that a fuzzy context contains a considerable number of fuzzy concepts. This makes it very timeconsuming to generate a fuzzy concept lattice, and it is very difficult to find important concepts. In addition, the fuzzy concept shows great

Spatiotemporal adaptive neural network for longterm forecasting of financial time series Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201208
Philippe Chatigny; JeanMarc Patenaude; Shengrui WangOptimal decisionmaking in social settings is often based on forecasts from time series (TS) data. Recently, several approaches using deep neural networks (DNNs) such as recurrent neural networks (RNNs) have been introduced for TS forecasting and have shown promising results. However, the applicability of these approaches is being questioned for TS settings where there is a lack of quality training

An evolutionary strategic weight manipulation approach for multiattribute decision making: TOPSIS method Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201119
Bapi Dutta; Son Duy Dao; Luis Martínez; Mark GohWeight information of the attributes plays a pivotal role in multiattribute decision making (MADM) problems. Oftentimes, a decision maker may try to manipulate this weight information to persuade a particular rank order of the alternatives of his/her interest. In the literature, this type of manipulation is known as strategic manipulation of the weight information. In this study, we consider the manipulation

Credal sets representable by reachable probability intervals and belief functions Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201119
Serafín MoralGarcía; Joaquín AbellánBelief functions and reachable probability intervals are theories based on imprecise probabilities that generalize classical probability theory. On the one hand, belief functions have been commonly used to deal with uncertainty and in the combination of information provided by different sources. On the other hand, reachable probability intervals have high expressive power, are easy to manage, and can

Two clustering methods based on the Ward's method and dendrograms with intervalvalued dissimilarities for intervalvalued data Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201117
Yu Ogasawara; Masamichi KonNumerous studies have focused on clustering methods for intervalvalued data, which is a type of symbolic data. However, limited attention has been awarded to a clustering method employing intervalvalued dissimilarity measures. To address this issue, herein, we propose two clustering approaches based on the Ward method using intervalvalued dissimilarity for the intervalvalued data. Each clustering

A logical reasoning based decision making method for handling qualitative knowledge Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201113
Shuwei Chen; Jun Liu; Yang XuSuccessful decisionmaking analysis needs to take both advantages of human analysts and computers, and human knowledge is usually expressed in a qualitative way. Computer based approaches are good at handling quantitative data, while it is still challenging on how to well structure qualitative knowledge and incorporate them as part of decision analytics. This paper develops a logical reasoning based

Note on topologies induced by coverings of approximation spaces Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201104
Michiro KondoWe consider topological properties of an approximation space U with a covering C of U. A topology τ is defined by use of covering C. We show that τ forms an Alexandrov topology and any member K of C is a closed subset with respect to τ. Moreover, we prove some fundamental properties of the topological space (U,τ). In particular, if the topological space (U,τ) satisfies one of the separation axioms

Fuzzy extensions of the dominancebased rough set approach Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201112
Marko Palangetić; Chris Cornelis; Salvatore Greco; Roman SłowińskiIn this paper, we first review existing fuzzy extensions of the dominancebased rough set approach (DRSA), and advance the theory considering additional properties. Moreover, we examine the application of Ordered Weighted Average (OWA) operators to fuzzy DRSA. OWA operators have shown a lot of potential in handling outliers and noisy data in decision tables, when they are combined with the indiscernibilitybased

Nondeterministic finite automata based on quantum logic: Language equivalence relation and robustness Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201109
Haihui Wang; Luyao Zhao; Ping LiAutomata theory based on quantum logic has been established by Ying. Nondeterministic fuzzy finite automata theory has been proposed by Cao, and further generalized by Pan et al. In this paper, we propose the notion of nondeterministic finite automaton based on quantum logic whose underlying structure is a complete orthomodular lattice l, called nondeterministic lvalued finite automaton (NlFA, for

On interval RO and (G,O,N)implications derived from interval overlap and grouping functions Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201021
Meng Cao; Bao Qing HuThis paper deals with two sorts of interval fuzzy implications derived from interval overlap and grouping functions, viz., interval RO and (G,O,N)implications. Firstly, interval ROimplications, preserving the residuation property, are the interval generalization of ROimplications induced by overlap functions. We investigate their properties and their correlations with interval automorphisms. Secondly

Local regression smoothers with setvalued outcome data Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201017
Qiyu Li; Ilya Molchanov; Francesca Molinari; Sida PengThis paper proposes a method to conduct local linear regression smoothing in the presence of setvalued outcome data. The proposed estimator is shown to be consistent, and its mean squared error and asymptotic distribution are derived. A method to build error tubes around the estimator is provided, and a small Monte Carlo exercise is conducted to confirm the good finite sample properties of the estimator

On a distribution form of subcopulas Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201021
Santi TasenaIn this work, we study the problem of (sub)copula estimation via continuity of the Sklar's correspondence. One benefit of this approach is that the estimator can be obtained from that of the corresponding (joint) distribution function via plugin method. Additional proof is not required. Our approach is to naturally embed the space of subcopulas into the space of distribution functions. This allows

Observability in locally vague environments Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201021
Mustafa DemirciIn this paper, we introduce observable Lfuzzy subsets of an Lfuzzy set in a locally vague environment, and give their axiomatization. In addition to this, lower and upper observability operators that enable us to approximate nonobservable Lfuzzy sets within some observable bounds are studied. In particular, we deal with their topological characterization, and expose that they can be identified

Label distribution feature selection for multilabel classification with rough set Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201015
Wenbin Qian; Jintao Huang; Yinglong Wang; Yonghong XieMultilabel learning deals with cases where every instance corresponds to multiple labels. The objective is to learn mapping from an instance to a relevant label set. Existing multilabel learning approaches assume that the significance for all related labels is same for every instance. Several problems of label ambiguity can be dealt with using multilabel learning, but some practical applications

Asymmetric dependence in the stochastic frontier model using skew normal copula Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201027
Zheng Wei; Erin M. Conlon; Tonghui WangIn this paper, a new skew normal copulabased stochastic frontier model (SFM) is proposed to investigate the asymmetric dependence among the disturbances U (representing technical inefficiency) and V (representing noise). By employing the skewnormal copula in SFM, the asymmetric joint behavior of U and V can be parameterized, thereby allowing the data to have the opportunity to determine the adequacy

Scalinginvariant maximum margin preference learning Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201019
Mojtaba Montazery; Nic WilsonOne natural way to express preferences over items is to represent them in the form of pairwise comparisons, from which a model is learned in order to predict further preferences. In this setting, if an item a is preferred to the item b, then it is natural to consider that the preference still holds after multiplying both vectors by a positive scalar (e.g., 2a≻2b). Such invariance to scaling is satisfied

Modus tollens with respect to uninorms: UModus Tollens Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201019
Isabel Aguiló; Juan Vicente Riera; Jaume Suñer; Joan TorrensIn fuzzy logic and approximate reasoning the inference rule given by the Modus Tollens usually derives into an inequality involving three logical operators: a conjunction, an implication function and a negation. Until now, in this scenario the conjunction has been commonly modeled by a tnorm, but recently the possibility of using a more general conjunction has been pointed out. In this work, we want

On subjective expected value under ambiguity Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201015
Radim Jiroušek; Václav KratochvílThe paper describes decisionmaking models based on a newly introduced notion of personal expected value. Such models exhibit the ambiguity aversion, which is controlled by a subjective parameter with the semantics of “the higher the aversion, the higher the coefficient”. For negative values of this parameter the models thus manifest a positive attitude to ambiguity. If this parameter equals zero,

A practical reliability design method considering the compound weight and loadsharing Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201001
Yao Li; Frank P.A. Coolen; Caichao ZhuReliability design is an important work in the early design stage of offshore wind turbines. Due to the incomplete considerations and poor feasibility of the drawbacks for existing methods, a set of the practical reliability design method is proposed in this paper. The time characteristics and many influential factors of units are considered in the design process. The influential factors of the system's

A novel quantum grasshopper optimization algorithm for feature selection Int. J. Approx. Reason. (IF 2.678) Pub Date : 20200903
Dong Wang; Hongmei Chen; Tianrui Li; Jihong Wan; Yanyong HuangFeature selection is an indispensable work to make the data mining more effective. It reduces the computational complexity and effectively improves the performance of learning models. The exhaustive algorithm and the greedy algorithm cannot adapt to the current increasing number of features when finding the potential optimal feature subset. Therefore, the feasible way for feature selection called swarm

Relationships between relationbased rough sets and belief structures Int. J. Approx. Reason. (IF 2.678) Pub Date : 20201014
YanLan Zhang; ChangQing LiAs two important methods used to deal with uncertainty, the rough set theory and the evidence theory have close connections with each other. The purpose of this paper is to examine relationships between the relationbased rough set theory and the evidence theory, and to present interpretations of belief structures in relationbased rough set algebras. The probabilities of relation lower and upper approximations

From equivalence queries to PAC learning: The case of implication theories Int. J. Approx. Reason. (IF 2.678) Pub Date : 20200922
Ramil Yarullin; Sergei ObiedkovIn Angluin's exactlearning framework, equivalence queries can be simulated by stochastic equivalence testing to achieve a probably approximately correct identification of an unknown concept. We present an analysis of the number of samples that need to be generated in the process leading to a theoretical improvement on an earlier approach. We apply this modification to a previously known probably approximately

Partialoverall dominance threeway decision models in intervalvalued decision systems Int. J. Approx. Reason. (IF 2.678) Pub Date : 20200909
Dandan Yang; Tingquan Deng; Hamido FujitaThreeway decisions are a generalization of classical decision theory and receive increasing attentions from various fields to handle decisionmaking problems, especially when involving in incomplete information. An interval is a typical notion of information representation with incompleteness and uncertainty. To measure the dominance degree of one interval dominating or being dominated by another

A latticebased representation of independence relations for efficient closure computation Int. J. Approx. Reason. (IF 2.678) Pub Date : 20200828
Linda C. van der Gaag; Marco Baioletti; Janneke H. BoltIndependence relations in general include exponentially many statements of independence, that is, exponential in the number of variables involved. These relations are typically characterised however, by a small set of such statements and an associated set of derivation rules. While various computational problems on independence relations can be solved by manipulating these smaller sets without the

Extended belief rulebased model for environmental investment prediction with indicator ensemble selection Int. J. Approx. Reason. (IF 2.678) Pub Date : 20200827
FeiFei Ye; Suhui Wang; Peter Nicholl; LongHao Yang; YingMing WangEnvironmental investment prediction is an effective solution to reduce the wasteful investments of environmental management. Since environmental management involves diverse environmental indicators, investment prediction modeling usually causes the curse of dimensionality and uses irrelevant indicators. A common solution to solve these problems is the use of indicator selection methods to select representative

A probabilistic deontic argumentation framework Int. J. Approx. Reason. (IF 2.678) Pub Date : 20200831
Régis Riveret; Nir Oren; Giovanni SartorWhat does it mean that something is probably obligatory? And how does it relate to the probability that it is permitted or prohibited? In this paper, we provide a possible answer by merging deontic argumentation and probabilistic argumentation into a probabilistic deontic argumentation framework. This framework allows us to specify a semantics for the probability of deontic statuses. The deontic argumentation

Efficient approaches for maintaining dominancebased multigranulation approximations with incremental granular structures Int. J. Approx. Reason. (IF 2.678) Pub Date : 20200820
Chengxiang Hu; Li ZhangIn practical decision making applications, it is computationally timeconsuming to maintain multigranulation approximations from scratch in dynamic ordered decision information systems (ODISs) with incremental granular structures consisting of the changing of granular structures by adding granular structures, or by adding an attribute set into each granular structure. The time consumed in the process

Semiring programming: A semantic framework for generalized sum product problems Int. J. Approx. Reason. (IF 2.678) Pub Date : 20200826
Vaishak Belle; Luc De RaedtTo solve hard problems, AI relies on a variety of disciplines such as logic, probabilistic reasoning, machine learning and mathematical programming. Although it is widely accepted that solving realworld problems requires an integration amongst these, contemporary representation methodologies offer little support for this. In an attempt to alleviate this situation, we position and motivate a new declarative

Thirty years of credal networks: Specification, algorithms and complexity Int. J. Approx. Reason. (IF 2.678) Pub Date : 20200821
Denis Deratani Mauá; Fabio Gagliardi CozmanCredal networks generalize Bayesian networks to allow for imprecision in probability values. This paper reviews the main results on credal networks under strong independence, as there has been significant progress in the literature during the last decade or so. We focus on computational aspects, summarizing the main algorithms and complexity results for inference and decision making. We address the