A multigranular linguistic content‐based recommendation model

Recommendation systems are a clear example of an e‐service that helps the users to find the most suitable products they are looking for, according to their preferences, among a vast quantity of information. These preferences are usually related to human perceptions because the customers express their needs, taste, and so forth to find a suitable product. The perceptions are better modeled by means of linguistic information due to the uncertainty involved in this type of information. In this article, we propose a content‐based recommendation model that will offer a more flexible context to improve the final recommendations where the preferences provided by the sources will be modeled by means of linguistic variables assessed in different linguistic term sets. The proposal consists of offering a multigranular linguistic context for expressing the preferences instead of forcing users to use a unique scale. Then the content‐based recommendation model will look for the most suitable product(s), comparing them with the customer(s) information according to its resemblance. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 419–434, 2007.     

Multiple-attribute group decision-making based on power Bonferroni operators of linguistic q-rung orthopair fuzzy numbers

In this paper, a new conception of linguistic q-rung orthopair fuzzy number (Lq-ROFN) is proposed where the membership and nonmembership of the q-rung orthopair fuzzy numbers ( q-ROFNs) are represented as linguistic variables. Compared with linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers, the Lq-ROFNs can more fully describe the linguistic assessment information by considering the parameter q to adjust the range of fuzzy information. To deal with the multiple-attribute group decision-making (MAGDM) problems with Lq-ROFNs, we proposed the linguistic score and accuracy functions of the Lq-ROFNs. Further, we introduce and prove the operational rules and the related properties characters of Lq-ROFNs. For aggregating the Lq-ROFN assessment information, some aggregation operators are developed, involving the linguistic q-rung orthopair fuzzy power Bonferroni mean (BM) operator, linguistic q-rung orthopair fuzzy weighted power BM operator, linguistic q-rung orthopair fuzzy power geometric BM (GBM) operator, and linguistic q-rung orthopair fuzzy weighted power GBM operator, and then presents their rational properties and particular cases, which cannot only reduce the influences of some unreasonable data caused by the biased decision-makers, but also can take the interrelationship between any two different attributes into account. Finally, we propose a method to handle the MAGDM under the environment of Lq-ROFNs by using the new proposed operators. Further, several examples are given to show the validity and superiority of the proposed method by comparing with other existing MAGDM methods.     

nk‐bags

We generalize the concept of bags by introducing the notion of n^k‐bags with membership functions ranging in P(N), the power set of positive integers. Consequently, a number of operations on n^k‐bags are defined and some characterizations are done. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 223–236, 2007.     

A multigranular hierarchical linguistic model for design evaluation based on safety and cost analysis

Before implementing a design of a large engineering system different design proposals are evaluated. The information used by experts to evaluate different options may be vague and/or incomplete. Although different probabilistic tools and techniques have been used to deal with these kinds of problems, it seems better to use the fuzzy linguistic approach to model vagueness and the Dempster‐Shafter theory of evidence for modeling incompleteness and ignorance. In the evaluation of alternative designs, different criteria can be considered. In this article an evaluation process is developed in terms of Safety and Cost analysis. Both criteria involve uncertainty, vagueness, and ignorance due to their nature. Therefore, we propose an evaluation process defined in a linguistic framework where both criteria will be conducted in different utility spaces, i.e., in a multigranular linguistic domain. Once the evaluation framework has been defined, we present an evaluation process based on a Multi‐Expert Multi‐Criteria decision model that will be able to deal with multigranular linguistic information without loss of information in order to evaluate different design options for an engineering system in a precise manner. Accordingly, we propose the use of a multigranular linguistic model based on the Linguistic Hierarchies presented by Herrera and Martínez (“A model based on linguistic 2‐tuples for dealing with multigranularity hierarchical linguistic contexts in multi‐expert decision‐making.” IEEE Trans Syst Man Cybern B 2001;31(2):227–234). © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1161–1194, 2005.     

A model of an information retrieval system with unbalanced fuzzy linguistic information

Most information retrieval systems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express the weights of queries and the relevance degrees of documents. However, to improve the system–user interaction, it seems more adequate to express these linguistic weights and degrees by means of unbalanced linguistic scales, that is, linguistic term sets with different discrimination levels on both sides of the middle linguistic term. In this contribution we present an information retrieval system that accepts weighted queries whose weights are expressed using unbalanced linguistic term sets. Then, the system provides the retrieved documents classified in linguistic relevance classes assessed on unbalanced linguistic term sets. To do so, we propose a methodology to manage unbalanced linguistic information and we use the linguistic 2‐tuple model as the representation base of the unbalanced linguistic information. Additionally, the linguistic 2‐tuple model allows us to increase the number of relevance classes in the output and also to improve the performance of the information retrieval system. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1197–1214, 2007.     

二元语义信息集结算子的性质分析

研究语言评价信患处理中有关二元语义集结算子的性质。首先描述了二元语义信患集结的有序加权平均(T-OWA)算子，并提出一种有序加权几何(T-OWG)算子；然后分析了T-OWA算子和T-OWG算子所具有的性质。该研究结果丰富了二元语义集结算子的分析方法。     

A possibility theory‐based approach to the handling of uncertain relations between temporal points

Uncertain relations between temporal points are represented by means of possibility distributions over the three basic relations precedes, equals, and follows. Operations for computing inverse relation, for composing relations, for combining relations coming from different sources and pertaining to the same temporal points, or for representing negative information are defined. An illustrative example of representation and reasoning with uncertain temporal relations is provided. This article shows how possibilistic temporal uncertainty can be handled in the setting of point algebra. Moreover, the article emphasizes the advantages of the possibilistic approach over a probabilistic approach previously proposed. This work does for the temporal point algebra what the authors previously did for the temporal interval algebra. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 157–179, 2007.     

Aggregation operators based on indistinguishability operators

This article gives a new approach to aggregating assuming that there is an indistinguishability operator or similarity defined on the universe of discourse. The very simple idea is that when we want to aggregate two values a and b we are looking for a value λ that is as similar to a as to b or, in a more logical language, the degrees of equivalence of λ with a and b must coincide. Interesting aggregation operators on the unit interval are obtained from natural indistinguishability operators associated to t‐norms that are ordinal sums. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 857–873, 2006.     

AGGREGATING FUZZY OPINIONS IN THE HETEROGENEOUS GROUP DECISION-MAKING ENVIRONMENT

ABSTRACT

We present a new information fusion algorithm for fusing fuzzy opinions in a heterogeneous group decision-making environment. The proposed information fusion algorithm has the following advantages: (1) It uses linguistic quantifiers based on the FN-IOWA operator to flexibly determine the weight w _i of the opinion of each expert E _i for aggregating the experts' fuzzy opinions. (2) The experts' opinions do not necessarily need to have a common intersection. (3) It does not need to use the Delphi method to adjust fuzzy numbers given by experts.     

基于新型语言评估标度的二元语义改进模型

为了有效地求解基于语言评价信息的多属性决策问题,针对目前常用的二元语义分析方法中存在的标度转换问题,提出一种处理语言评价信息的新型复合标度(该标度综合了指数标度和-n～n标度的优点),建立了基于新型复合标度的二元语义改进模型.算例验证结果表明,新型复合标度为处理定性的语言评价信息提供了科学依据,所提出的改进模型可以有效提高决策结果的精度和可信度.     

Local identification of prototypes for genetic learning of accurate TSK fuzzy rule‐based systems

This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics‐based Takagi–Sugeno–Kang (TSK) rules. This allow us to start from prototypes considering the interaction between input and output variables and taking into account the fuzzy nature of the TSK rules. To do so, a two‐stage evolutionary algorithm based on MOGUL (a methodology to obtain Genetic Fuzzy Rule‐Based Systems under the Iterative Rule Learning approach) has been developed to consider the interaction between input and output variables. The first stage performs a local identification of prototypes to obtain a set of initial local semantics‐based TSK rules, following the Iterative Rule Learning approach and based on an evolutionary generation process within MOGUL (taking as a base some initial linguistic fuzzy partitions). Because this generation method induces competition among the fuzzy rules, a postprocessing stage to improve the global system performance is needed. Two different processes are considered at this stage, a genetic niching‐based selection process to remove redundant rules and a genetic tuning process to refine the fuzzy model parameters. The proposal has been tested with two real‐world problems, achieving good results. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 909–941, 2007.     

Assessment of four modifications of a novel indexing technique for case‐based reasoning

In this article, we investigate four variations (D‐HS^M, D‐HS^W, D‐HS^E, and D‐HS^EW) of a novel indexing technique called D‐HS designed for use in case‐based reasoning (CBR) systems. All D‐HS modifications are based on a matrix of cases indexed by their discretized attribute values. The main differences between them are in their attribute discretization stratagem and similarity determination metric. D‐HS^M uses a fixed number of intervals and simple intersection as a similarity metric; D‐HS^W uses the same discretization approach and a weighted intersection; D‐HS^E uses information gain to define the intervals and simple intersection as similarity metric; D‐HS^EW is a combination of D‐HS^E and D‐HS^W. Benefits of using D‐HS include ease of case and similarity knowledge maintenance, simplicity, accuracy, and speed in comparison to conventional approaches widely used in CBR. We present results from the analysis of 20 case bases for classification problems and 15 case bases for regression problems. We demonstrate the improvements in accuracy and/or efficiency of each D‐HS modification in comparison to traditional k‐NN, R‐tree, C4,5, and M5 techniques and show it to be a very attractive approach for indexing case bases. We also illuminate potential areas for further improvement of the D‐HS approach. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 353–383, 2007.     

Some q-rung orthopair uncertain linguistic aggregation operators and their application to multiple attribute group decision making

q-Rung orthopair fuzzy sets (q-ROFSs), originally presented by Yager, are a powerful fuzzy information representation model, which generalize the classical intuitionistic fuzzy sets and Pythagorean fuzzy sets and provide more freedom and choice for decision makers (DMs) by allowing the sum of the $q\mathrm{t}\mathrm{h}$ power of the membership and the $q\mathrm{t}\mathrm{h}$ power of the nonmembership to be less than or equal to 1. In this paper, a new class of fuzzy sets called q-rung orthopair uncertain linguistic sets (q-ROULSs) based on the q-ROFSs and uncertain linguistic variables (ULVs) is proposed, and this can describe the qualitative assessment of DMs and provide them more freedom in reflecting their belief about allowable membership grades. On the basis of the proposed operational rules and comparison method of q-ROULSs, several q-rung orthopair uncertain linguistic aggregation operators are developed, including the q-rung orthopair uncertain linguistic weighted arithmetic average operator, the q-rung orthopair uncertain linguistic ordered weighted average operator, the q-rung orthopair uncertain linguistic hybrid weighted average operator, the q-rung orthopair uncertain linguistic weighted geometric average operator, the q-rung orthopair uncertain linguistic ordered weighted geometric operator, and the q-rung orthopair uncertain linguistic hybrid weighted geometric operator. Then, some desirable properties and special cases of these new operators are also investigated and studied, in particular, some existing intuitionistic fuzzy aggregation operators and Pythagorean fuzzy aggregation operators are proved to be special cases of these new operators. Furthermore, based on these proposed operators, we develop an approach to solve the multiple attribute group decision making problems, in which the evaluation information is expressed as q-rung orthopair ULVs. Finally, we provide several examples to illustrate the specific decision-making steps and explain the validity and feasibility of two methods by comparing with other methods.     

Aggregating expert predictions in a networked environment

This paper describes an approach for combining the classifications or predictions of n local experts into a single composite prediction. We describe a Java-based application that allows a user to select up to n prediction experts that provide information for assigning an object to one of two predetermined groups. An advantage of this type of application is that it is capable of interacting with the Internet in a relatively seamless way. We examine the accuracy and robustness of our technique by comparing the classification accuracy of our technique, a maximum entropy-based aggregation technique, and four classification methods on a real-world, two-group data-set concerned with bank failure prediction. The classificaiton methods studied in this work include Quinlan's C4.5 decision-tree classifier, logistic regression, mahalanobis distance measures, and a neural network classifier. Our model includes a fundamental component (i.e., a transaction manager) that helps improve the general performance of applications that perform network-based classification. This component is found to provide reliable and secure connections along with ways to direct traffic across the Internet. Our results suggest three major contributions: (1) a transaction manager increases the flexibility of a network-based classifier since it is capable of transacting with one or more specific types of prediction expert(s) over the Internet; (2) our approach tends to be more accurate than the individual classification methods we examined; and, (3) our approach can outperform a recently introduced statistically based aggregation technique.Scope and purposeThe emergence of the Internet has produced a need for employing new types of programming and research tools that are capable of accessing information resources located throughout the world. There is only a limited amount of research available in this area and this work describes a network-based tool that solves a two-group classification problem. The two-group classification problem in discriminant analysis is concerned with developing a rule for predicting to which of k=2 mutually exclusive groups an observation of unknown origin belongs. This problem commonly occurs in business and other areas, and a plethora of statistical and artificial intelligence (AI) techniques exist to help decision-makers effectively analyze their data. A number of recent studies have compared the classificatory performance of various AI techniques to the more traditional statistical techniques, however, decision makers are left in somewhat of a quandary about which of the many available classification techniques to use to solve a specific classification problem. This paper proposes a new aggregation technique that focuses on combining or aggregating the predictions from multiple classification techniques into a single composite prediction. Our approach provides a simple method for aggregating expert predictions coming from remote locations by combining Java and Common Object Request Broker Architecture (CORBA) into a general classification tool. Object-oriented models developed using Java are platform independent and can be easily modified. CORBA provides the services necessary to establish and manage network connections. Computational results show that our technique outperforms a recently introduced maximum entropy-based aggregation technique using a real-world data set.     

Using B<Superscript>+</Superscript>-trees for processing of line segments in large spatial databases

Points, lines, and regions are the three basic entities for constituting vector-based objects in spatial databases. Many indexing methods (G-tree, K-D-B tree, Quad-tree, PMR-tree, Grid-file, R-tree, and so on) have been widely discussed for handling point or region data. These traditional methods can efficiently organize point or region objects in a space into a hashing or hierarchical directory. They provide efficient access methods to meet the requirement of accurate retrievals. However, two problems are encountered when their techniques are applied to deal with line segments. The first is that representing line segments by means of point or region objects cannot exactly and properly preserve the spatial information about the proximities of line segments. The second problem is derived from the large dead space and overlapping areas in external and internal nodes of the hierarchical directory caused by the use of rectangles to enclose line objects. In this paper, we propose an indexing structure for line segments based on B ⁺ -tree to remedy these two problems. Through the experimental results, we demonstrate that our approach has significant improvement over the storage efficiency. In addition, the retrieval efficiency has also been significantly prompted as compared to the method using R-tree index scheme. These improvements derive mainly from the proposed data processing techniques and the new indexing method.     

Comparative study on adaptive rational macromodels of highly resonant structures

Two macromodeling techniques using rational bases are investigated to accurately predict the natural frequencies of highly resonant microwave structures. Three methods are proposed and compared to calculate pole‐free solutions to the Thiele continued fraction and vector fitting pole‐residue models of the characteristic equation det[Z(s)]. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.     

Pyramid collaborative filtering technique for an intelligent autonomous guide agent

This article presents an autonomous guide agent that can observe a community of learners on the web, interpret the learners' inputs, and then assess their sharing. The goal of this agent is to find a reliable helper (tutor or other learner) to assist a learner in solving his task. Despite the growing number of Internet users, the ability to find helpers is still a challenging and important problem. Although helpers could have much useful information about courses to be taught, many learners fail to understand their presentations. For that, the agent must be able to deal autonomously with the following challenges: Do helpers have information that the learners need? Will helpers present information that learners can understand? And can we guarantee that these helpers will collaborate effectively with learners? We have developed a new filtering framework, called a pyramid collaborative filtering model, to whittle the number of helpers down to just one. We have proposed four levels for the pyramid. Moving from one level to another depends on three filtering techniques: domain model filtering, user model filtering, and credibility model filtering. A new technique is filtering according to helpers' credibilities. Our experiments show that this method greatly improves filtering effectiveness. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1065–1082, 2007.     

基于Pythagorean模糊语言集多属性群决策方法

为了解决直觉语言集不能够处理隶属于与非隶属于语言值的程度之和大于1的情况,提出了Pythagorean模糊语言集。针对Pythagorean模糊语言信息的集成问题,定义了Pythagorean模糊语言数的运算法则及其得分函数、精确函数,提出了Pythagorean模糊语言加权平均（PFLWA）算子、Pythagorean模糊语言有序加权平均（PFLOWA）算子、Pythagorean模糊语言混合平均（PFLHA）算子,并讨论了相应的性质。然后基于组合权重的PFLWA算子与拓展的TOPSIS,提出了两种多属性群决策方法。最后通过实例验证了该方法的有效性。     

Generalized consistency and intensity vectors for comparison matrices

A crucial problem in a decision‐making process is the determination of a scale of relative importance for a set X = {x₁, x₂,..., x_n} of alternatives either with respect to a criterion C or an expert E. A widely used tool in Multicriteria Decision Making is the pairwise comparison matrix A = (a_ij), where a_ij is a positive number expressing how much the alternative x_i is preferred to the alternative x_j. Under a suitable hypothesis of no indifference and transitivity over the matrix A = (a_ij), the actual qualitative ranking on the set X is achievable. Then a vector w may represent the actual ranking at two different levels: as an ordinal evaluation vector, or as an intensity vector encoding information about the intensities of the preferences. In this article we focus on the properties of a pairwise comparison matrix A = (a_ij) linked to the existence of intensity vectors. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1287–1300, 2007.