Linguistic Quantifiers Research Articles

Defining personalized concepts for XBRL using iPAD‐drawn fuzzy sets

SummaryAn efficient and effective analysis of business data requires a better understanding of what the data represents, and to what degree. A human‐like way of accomplishing that without being too detailed yet learning more about data content is to summarize and map the data into concepts familiar to a person performing analysis. Processes of summarization help identify the most essential facts that are embedded in the data. All this is of significant importance for analysis of large amounts of business data required to make good and sound financial decisions.There are two aspects enabling more comprehensive yet easier processing of data: a standardized representation format of financial data; and a human‐friendly way of defining concepts and using them for building personalized models representing processing data. The first of the aspects has been addressed by the eXtensible Business Reporting Language (XBRL)—a standardized format of defining, representing and exchanging corporate and financial information. The second aspect is related to providing individuals with the ability to gain understanding of data content via determining a degree of truth of statements summarizing data based on their own perception of concepts they are looking for.In this paper, we introduce a tablet application—Tablet‐based input of Fuzzy Sets (TiFS)—and demonstrate its usefulness for entering personalized definitions of concepts and terms that enable a quick analysis of financial data. Such analysis means utilization of soft queries and operations of aggregation that extract and summarize the data and present it in a form familiar to analysts. The application allows for defining concepts and terms with ‘finger‐made’ drawings representing a person's perception of concepts. Further, these definitions are used to build summarization statements for exploring XBRL data. They are equipped with ‘drawn’ definitions of linguistic terms (e.g. LARGE, SMALL, FAST) and linguistic quantifiers (e.g. ALL, MOSTLY), and enable summarization of data content from the perspective of a user's interests. The ‘drawn’ linguistic terms and quantifiers represent membership functions of fuzzy sets. Utilization of fuzzy sets allows for performing operations of data summarization in a human‐like way. The application of TiFS illustrates ease of inputting personalized definitions of concepts and their influence on the interpretation of data. This introduces aspects of personalization and adaptation of artificial intelligence systems to perceptions and views of individuals. The proposed application is used to perform a basic analysis of an XBRL document.

Preference similarity network structural equivalence clustering based consensus group decision making model

Social network analysis (SNA) methods have been developed to analyse social structures and patterns of network relationships, although they have been least explored and/or exploited purposely for decision-making processes. In this study, we bridge a gap between SNA and consensus-based decision making by defining undirected weighted preference network from the similarity of expert preferences using the concept of ‘structural equivalence’. Structurally equivalent experts are represented using the agglomerative hierarchical clustering algorithm with complete link function, thus intra-clusters’ experts are high in density and inter-clusters’ experts are rich in sparsity. We derive cluster consensus based on internal and external cohesions, while group consensus is obtained by identifying the highest level consensus at optimal level of clustering. Thus, the clustering based approach to consensus measure contributes to present homogeneity of experts preferences as a whole. In the event of insufficient group consensus state, we construct a feedback mechanism procedure based on clustering that consists of three main phases: (1) identification of experts that contribute less to consensus; (2) identification of a leader in the network; and (3) advice generation. We make use of the centrality concept in SNA as a way of determining the most important person in a network, who is presented as a leader to provide advices in the feedback process. It is proved that the implementation of the proposed feedback mechanism increases consensus and, because of the bounded condition of consensus measure, convergence to sufficient group agreement is guaranteed. The centrality concept is also applied in the construction of a new aggregation operator, namely as cent-IOWA operator, that is used to derive the collective preference relation from which the feasible alternative of consensus solution, based on the concept of dominance, is achieved according to a majority of the central experts in the network, which is represented in this paper by the linguistic quantifier ‘most of.’ For validation purposes, an existing literature study is used to perform a comparative analysis from which conclusions are drawn and explained.

Landfill site selection using combination of fuzzy logic and multi-attribute decision-making approach

During the last decades, growth of urbanization and industrialization led to an increase in solid waste generation. Landfilling is the most prevalent ultimate disposal method for the municipal solid wastes in developing countries. The rapid municipal solid waste generation in Markazi province (central part of Iran) causes the need for precision in finding a suitable landfill site selection. In the present study, 12 factors (environmental and socioeconomic factors) have been applied to select the landfill site in Markazi province, Iran. The different methods including the analytic network process (ANP) combined with fuzzy linguistic quantifier, ordered weighted average (OWA), and weighted linear combination (WLC) approach in geographic information system was applied to find an appropriate landfill site. The OWA operator function permits the evaluation of the wide spectrum of consequences (with different scenario) obtained from different management strategies. Results revealed that integration of fuzzy logic, ANP, and OWA provides flexible and better ideas compared to the Boolean logic and WLC to select a suitable landfill site.

English

In this paper, we propose a decision support system for enhancing scrum methodology based on fuzzy logic. Scrum is a very popular agile methodology within the software and product development. In the basic scrum, requirements that describe a certain task do not have a clear interpretation. Also, the traditional model does not take into account the experience of the developers nor the logical dependencies of input variables. Fuzzy inference is particularly useful for this purpose, because it incorporates logic in inference process and inputs are presented using linguistic quantifiers. The proposed system consists of three main components: a fuzzy inference system, an aggregation operator and a feedback function. The aggregation function is used to aggregate task predictions in a single value that uniquely represent a specific task, while a feedback is employed to adjust an input variable to improve system performance. Furthermore, the proposed system is simulated with randomly generated inputs in order to analyse its behaviour. The predictions of the system are more accurate and with smaller deviation in the final iterations.JEL Classification: C63, D81, L86

Role of linguistic quantifier and digitally approximated Laplace operator in infrared based ship detection

Due to high sensitivity of strategical importance of sea border, detection of motion of enemy’s military troops and arms may play a crucial role. Attack of 26, November, 2008 on Mumbai, commercial capital of India, is the result of such type of piercing of water territory. Hence, this paper proposed new Threshold based ship detection method. Infrared image of ship in sea water used as input. To specify appropriate threshold Yager’s Linguistic quantifier ‘most’, ‘at least half’, and ‘as many as possible’ are used. Moreover edge based segmentation of thresholding images is carried out by using Laplacian operator. It is observed threshold generated by ‘at least half’ and ‘as many as possible’ quantifier are closer to visual heuristic threshold. It is also found linguistic quantifier based thresholding has generated clear edge to separate ship from background along with digital approximation of Laplace operator than other methods.

A Consensus Approach to the Sentiment Analysis Problem Driven by Support-Based IOWA Majority

In group decision making, there are many situations where the opinion of the majority of participants is critical. The scenarios could be multiple, like a number of doctors finding commonality on the diagnose of an illness or parliament members looking for consensus on an specific law being passed. In this article, we present a method that utilizes induced ordered weighted averaging (IOWA) operators to aggregate a majority opinion from a number of sentiment analysis (SA) classification systems, where the latter occupy the role usually taken by human decision-makers as typically seen in group decision situations. In this case, the numerical outputs of different SA classification methods are used as input to a specific IOWA operator that is semantically close to the fuzzy linguistic quantifier ‘most of’. The object of the aggregation will be the intensity of the previously determined sentence polarity in such a way that the results represent what the majority think. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier ‘most’ () proved to yield superior results compared to those achieved when utilizing other techniques commonly applied when some sort of averaging is needed, such as arithmetic mean or median techniques.

A Fuzzy based Model for Effort Estimation in Scrum Projects

This paper aims to utilize the fuzzy logic concepts to improve the effort estimation in Scrum framework and in turn add a significant enhancement to Scrum. Scrum framework is one of the most popular agile methods in which the team accomplishes their work by breaking down the work into a series of sprints. In Scrum, there are many factors that have a significant influence on the effort estimation of each task in a Sprint. These factors are: Development Team Experience, Task Complexity, Task Size, and Estimation Accuracy. These factors are usually presented using linguistic quantifiers. Therefore, this paper utilizes the fuzzy logic concepts to build a fuzzy based model that can improve the effort estimation in Scrum framework. The proposed model includes three components: fuzzifier, inference engine, and defuzzifier. In addition, the proposed model takes into consideration the feedback that is resulted from comparing the estimated effort and the actual effort. The researcher designed the proposed model using MATLAB. The proposed model is applied on three Sprints of a real software development project to present how the proposed model works and to show how it becomes more accurate over time and gives a better effort estimation. In addition, the Scrum Master and the development team can use the proposed model to monitor the improvement in effort estimation accuracy over the project life.

On the road toward innovative solutions: Supporting agents via some fuzzy group decision models

We deal with an important, yet a very challenging problem of whether, and how the process of attaining some innovative solution in a particular setting can be modeled in a relatively intuitively appealing, simple and constructive way, and then used for the support of such processes. We assume a multiagent group decision making setting, and a possibility of imprecise information and testimonies provided by the agents. For simplicity and generality, fuzzy preferences and fuzzy majority are assumed. Basically, we assume a (finite, relatively small) set of options and a set of agents, human beings or software entities, who present their testimonies as fuzzy preference relations which are quite general and can encompass many different forms. We seek and option or a set options that is somehow best acceptable to the whole group of agents, with respect to their preferences. This setting, with a relatively small number of options and agents is characteristic for many real world problems exemplified by making decisions by humans in committees, groups of experts, etc. We also assume that a fuzzy majority is employed. It is meant in Kacprzyk’s sense as equated with a fuzzy linguistic quantifier exemplified by “most”, “almost all”, “much more than a half”, etc. dealt with using, for instance, Zadeh’s calculus of linguistically quantified propositions, Yager’s OWA operators, etc. We consider some group decision solution concepts in such a setting, concentrating of Kacprzyk’s fuzzy Q-cores which is a fuzzy set of options which are not (strongly) defeated in pairwise comparisons (in the sense that other options are preferred over them) by the required fuzzy majority Q, e.g. “most”, of (important) agents. In this setting, we take into account all preference relations of the particular agents, just adding some relevance of options and importance of agents which are assumed to be given, for instance by domain experts. This can be viewed as a reflection of a so called status quo bias which, well known from psychology, which is an emotional bias with a preference for the current state of affairs, the principle of minimal change, etc. We develop some additional setting that could explicitly or implicitly make it possible to allow innovative group decisions, with the innovation considered to be an opposite to the status quo. We develop a simple yet effective and efficient approach in which – in addition to the relevance of options and importance of agents – there is another aspect taken into account. Namely, we derive first a social fuzzy preference relation, derived by some aggregation of the individual fuzzy preference relations, that represents the group testimony, which can be viewed as representing some “consensus”. Then, we calculate for each agent’s individual fuzzy preference relations its distance to that group (consensory) preference relation. Then, we assign additional weights to the particular individual preference relations, in addition to the weights corresponding to the relevance of options and importance of individuals. One can then argue that, from the innovation point of view, a solution that is best acceptable by, for instance, most agents whose preferences are close to the consensory group fuzzy preference relation, would rather reflect a conservative attitude, we reformulate the concept of the fuzzy Q-core that would more take into account the agents’ individual fuzzy preference relations that are farer from the consensory social fuzzy preference relation. Our analysis is exemplified by an application for real estate decision making.

Robust decision making using intuitionistic fuzzy numbers

Robustness is defined as a system’s ability to withstand under disturbances. In real-life applications, where problem parameters are often uncertain, incorporating robustness in decision making is important. In this study, we propose a robust decision making (RDM) approach using intuitionistic trapezoidal fuzzy number (ITrFN). Fuzzy linguistic quantifier (FLQ) is used in the proposed approach to compute the uncertain optimism degree of the decision maker. Initially, decision maker expresses his/her opinion using linguistic terms, which are presented numerically using ITrFNs. The aggregated ITrFN for each of the alternatives is evaluated using intuitionistic trapezoidal fuzzy ordered weighted averaging operator (ITrFOWA). Then, we find out the expected value and variance of the aggregated ITrFN for each alternative, which are subsequently used for robust decision making. A collective measure of these two values of each alternative is considered to find an interval of the corresponding alternative, known as optimal interval. Alternative with maximum optimal interval is selected as the robust solution. Applicability of the proposed approach has been demonstrated on a site selection problem of nuclear power plant. Site selection for installing a nuclear power plant has become a crucial problem throughout the world, especially after the Fukushima (2011) and Chernobyl (1986) nuclear disasters.

Mapping the likelihood of foot-and-mouth disease introduction along the border between Brazil and Paraguay

Abstract: The objective of this work was to apply fuzzy majority multicriteria group decision-making to determine risk areas for foot-and-mouth disease (FMD) introduction along the border between Brazil and Paraguay. The study was conducted in three municipalities in the state of Mato Grosso do Sul, Brazil, located along the border with Paraguay. Four scenarios were built, applying the following linguistic quantifiers to describe risk factors: few, half, many, and most. The three criteria considered to be most likely to affect the vulnerability to introduction of FMD, according to experts' opinions, were: the introduction of animals in the farm, the distance from the border, and the type of property settlements. The resulting maps show a strong spatial heterogeneity in the risk of FMD introduction. The used methodology brings out a new approach that can be helpful to policy makers in the combat and eradication of FMD.

A Similarity Classifier with Bonferroni Mean Operators

A similarity classifier based on Bonferroni mean based operators is introduced. The new Bonferroni mean based variant of the similarity classifier is also extended to cover a new Bonferroni-OWA variant. The new Bonferroni-OWA based similarity classifier raises the question of how to accomplish the weighting needed and for this reason we also examine a number of linguistic quantifiers for weight generation. The new proposed similarity classifier variants are tested on four real world medical research related data sets. The results are compared with results from two previously presented similarity classifiers, one based on the generalized mean and another based on an arithmetic mean operator. The results show that comparatively better classification accuracy can be reached with the proposed new similarity classifier variants.

Generalized Quantifiers in the Context of Rough Set Semantics

Looking back to Prof. Zadeh’s paradigm of Computing with Words (CWW) [28, 29, 30], one can notice that the initial attempt of such an endeavour was to set up a basic vocabulary of linguistic words, and fix their semantics based on fuzzy sets. Then a grammar was proposed to generate compound linguistic expressions based on the primitive ones, and simultaneously based on the semantic interpretations of those basic linguistic expressions a general scheme for the semantics of the rest of linguistic expressions were proposed. Sentences involving linguistic quantifiers and vague predicates constitute a fragment of natural language. In this paper, we choose this fragment of the natural language, and explore the semantics from the perspective of rough sets [13, 14, 16, 17, 18, 21]. We fix a set of basic crisp quantifiers, mainly of proportional kind. A set of vague quantifiers are proposed to lie in a close vicinity of those crisp quantifiers in the sense that a particular vague quantifier can be visualized as a blurred, may be called rough, image of a set of crisp quantifiers. Address for correspondence: Institute of Mathematics, University of Warsaw, 02 Banacha, 02-097 Warsaw, Poland ∗This work has been carried out during the ERCIM Alain Benseossan fellowship. †This work was partially supported by the Polish National Science Centre (NCN) grants DEC-2011/01/D/ST6/06981, DEC2012/05/B/ST6/03215, DEC-2013/09/B/ST6/01568 as well as by the Polish National Centre for Research and Development (NCBiR) under the grant O ROB/0010/03/001. 214 S. Dutta and A. Skowron / Generalized Quantifiers with Rough Set Semantics Semantics of the rest of the vague quantifiers can be obtained based on the subjective perception of the interrelations among the (vague) quantifiers.

Linguistic quantifiers modeled by interval-valued intuitionistic Sugeno integrals1

Ying’s model of linguistic quantifiers based on Sugeno integral is generalized to interval-valued intuitionistic Sugeno integral, the truth value of a quantified proposition is evaluated by using interval-valued intuitionistic Sugeno integral. Some logical properties of linguistic quantifiers in this model are discussed, and some application examples in uncertainty decision making and linguistic summarization of data are presented.

Siting MSW landfills using MCE methodology in GIS environment (Case study: Birjand plain, Iran)

The rapid municipal solid waste growth of Birjand plain causes to find an appropriate site selection for the landfill. In order to reduce the negative impacts of waste, the use of novel tools and technologies to gain a suitable site for landfill seems imperative. The present paper aimed to exhibits the Multi Criteria Evaluation (MCE) for the landfill site selection of the Birjand plain because till date a suitable action has not been implicated. In the present research, the parameters such as environmental and socio-economical factors have been used. The factors like slope, water resources, soil parameters, landuse, fault and protected areas in the model of effective environmental criteria and the factors viz. distance from road, urban areas, village, airport, historical place, and industries in the model of socio-economic criteria were investigated and with the use of Weighted Linear Combination (WLC) and Analytical Network Process (ANP) models were compounded and according to the Ordered Weighted Averaging (OWA) and Fuzzy Linguistic Quantifier (LQ) were aggregated. The paper focuses on the OWA method as well as an approach for integrating Geographic Information System (GIS) and OWA. OWA has been developed as a generalization of multi-criteria combination. In this study we attained comparable data via the technique of ANP and five scenarios of OWA method were used. The results of field studies, fifth scenario for the study area proposed. Based on the research findings, OWA method had a great potential and flexibility in the modeling of the complex decision-making problems.

Linguistic summarization of the contents of Web server logs via the Ordered Weighted Averaging (OWA) operators

Analyses of Web server logs may be very useful and are often needed for designers and analysts of computer networks who try to design and optimize the functioning of the systems, safeguard them against attacks, provide for the most effective and efficient access path for the users/customers, etc. Obviously, they are also an interesting research problem that can be viewed from many perspectives, ranging from just a general analyses to topic focused analyses that are aimed at just a specific aspect. In traditional approaches, various statistics are computed and used for analytic and design purposes. Sometimes, visualization tools are also employed. In this paper we present the use of verbalization of results of Web server log data analysis/mining through linguistic data summaries based on fuzzy logic with linguistic quantifiers. Linguistic summaries of both static and dynamic analyses are presented, with an emphasis on the latter. We extend our previous works by employing as an aggregation tool the Ordered Weighted Averaging (OWA) operators due to Yager [52]. We present some examples of potentially interesting linguistic summaries of Web server logs, and indicate their possible assignment to different classes.

A spatial analysis model to assess the feasibility of short rotation forestry fertigated with urban wastewater: Basilicata region case study

The large-scale cultivation of energy crops irrigated with non-conventional water resources could reduce the negative impacts of fossil fuel use, while still saving potable supplies and decreasing pollution in surface water, particularly in water-deficient environments, like the Mediterranean region. Energy planning is a complex process involving multiple decision makers and criteria. Given the spatial nature of the problem, the research proposes a spatial analysis model to assess the agronomic and economic feasibility of vegetation filter systems in Basilicata region, southern Italy. The model chosen for land suitability analysis is the ordered weighted averaging (OWA) with the use of linguistic quantifiers. The suitability map obtained from the OWA model was used as input in the spatial analysis functions to quantify the productivity and irrigation needs of the species, the potential irrigable service area of the wastewater treatment plants (WWTPs), as well as the distances between them and SRF, which are all key elements in the economic evaluation. The results show that the distance is the main element that influences the feasibility: only 25 out of 163 WWTPs are cost-effective and can actually irrigate 864ha of SRF. The research demonstrates that there is a great potential for bio-energy development in the region with significant economic advantages; in fact, there is a large number of sites with positive NPV up to 50,876.43€/ha and payback period between 3 and 10 years. The implementation of vegetation filter systems could create chains with a high number of local actors (farmers, intermediaries, forest nurseries, etc.) and contribute to promoting territorial development and employment.

Fuzziness in database management systems: Half a century of developments and future prospects

This comprehensive, bird's view research note combines the state of the art, a brief presentation of the history and some original solutions, and position like views of some prospective future developments of one of the most relevant and interesting areas related to the use of fuzzy logic in database management systems, notably in its querying component, and – to some extent – in a broader issue of data and information management. We briefly summarize the roots of those new applications of fuzzy logic, more relevant proposals and development in the context of fuzzification of the basic relational database model, and then some of its further generalizations. We particularly focus on fuzzy querying as a human consistent and friendly way of retrieving information due to real human intentions and preferences expressed in natural language represented via fuzzy logic and possibility theory. We mention some extensions, notably fuzzy queries with linguistic quantifiers, and point their close relation to linguistic summaries. As for newer, prospective developments, we mainly focus on bipolar queries that can accomodate the users' intentions and preferences involving some sort of a required and desired, mandatory and optional, etc. conditions. We show various ways of handling such queries. We conclude with some brief position statements of our view on relevant and promising directions, and challenges.

A multi-criteria spatial deprivation index to support health inequality analyses.

BackgroundDeprivation indices are useful measures to analyze health inequalities. There are several methods to construct these indices, however, few studies have used Geographic Information Systems (GIS) and Multi-Criteria methods to construct a deprivation index. Therefore, this study applies Multi-Criteria Evaluation to calculate weights for the indicators that make up the deprivation index and a GIS-based fuzzy approach to create different scenarios of this index is also implemented.MethodsThe Analytical Hierarchy Process (AHP) is used to obtain the weights for the indicators of the index. The Ordered Weighted Averaging (OWA) method using linguistic quantifiers is applied in order to create different deprivation scenarios. Geographically Weighted Regression (GWR) and a Moran’s I analysis are employed to explore spatial relationships between the different deprivation measures and two health factors: the distance to health services and the percentage of people that have never had a live birth. This last indicator was considered as the dependent variable in the GWR. The case study is Quito City, in Ecuador.ResultsThe AHP-based deprivation index show medium and high levels of deprivation (0,511 to 1,000) in specific zones of the study area, even though most of the study area has low values of deprivation. OWA results show deprivation scenarios that can be evaluated considering the different attitudes of decision makers. GWR results indicate that the deprivation index and its OWA scenarios can be considered as local estimators for health related phenomena. Moran’s I calculations demonstrate that several deprivation scenarios, in combination with the ‘distance to health services’ factor, could be explanatory variables to predict the percentage of people that have never had a live birth.ConclusionsThe AHP-based deprivation index and the OWA deprivation scenarios developed in this study are Multi-Criteria instruments that can support the identification of highly deprived zones and can support health inequalities analysis in combination with different health factors. The methodology described in this study can be applied in other regions of the world to develop spatial deprivation indices based on Multi-Criteria analysis.

Ranking of Fuzzy Similar Faces Using Relevance Matrix and Aggregation Operators

Abstract In perception based imaging, Sketching With Words (SWW) is a well-established methodology in which the objects of computation are fuzzy geometric objects (f-objects).The problem of facial imaging of criminal on the basis of onlooker statement is not lack of method and measures but the modeling of onlooker(s) mind set. Because the onlooker has to give statements about different human face parts like forehead, eyes, nose, and chin etc.The concept of fuzzy similarity (f-similarity) and proper aggregation of components of face may provide more flexibility to onlooker(s). In proposed work onlooker(s) statement is recorded. Thereafter it is compared with existing statements. The f-similarity with different faces in database is estimated by using ‘as many as possible’ linguistic quantifier. Three types of constraints over size of parts of face ‘small’, ‘medium’, and ‘large’ are considered. Possibilistic constraints with linguistic hedges and negation operator like ‘very long’, ‘not long’, ‘not very long’ etc. are used. Moreover we have generated ranking of alike faces in decreasing order by using the concepts of f-similarity and relevance matrix.

A Web based System for Cricket Talent Identification, Enhancement and Selection (C-TIES)

Abstract Cricket is an extremely popular game. More than a million cricketers play cricket daily in India alone and aspire to become professional cricketers. Cricket Talent identification and enhancement is a challenging problem due to lack of quality coaches, meagre infrastructural facilities, and poor linkages of coaching academies & cricket authorities. In India, the problem is even tougher as the majority of the population resides in villages. Many of talented players do not get timely recognition of cricket boards’ authorities thus amounting to waste of talent. Many keep on pursuing cricket despite of being non-talented. Due to lack of application of appropriate scientific methods the selection process is also criticized as biased by many. In this paper, we present a web based system viz. Cricket Talent Identification, Enhancement and Selection (C-TIES) for addressing the above issues. C-TIES utilize a cricket talent knowledgebase of experts’ opinions aggregated by applying OWA Aggregation Operator and Relative Fuzzy Linguistic Quantifier (RFLQ). The C-TIES system classifies the cricket talent level of an enthusiast into five different classes by applying Normalized Adequacy Coefficient (NAC). The Talent Enhancement and Talent Selection subsystems also uses appropriate algorithms based on OWA, RFLQ and NAC to respectively enable identification of weaknesses in a player and select most talented n-players from a larger group of players. Thus, system reduces the time for identifying weaknesses and also provides a relatively better unbiased selection method for short listing players. The system has been developed using Struts 2.0, Hibernate, J2EE, Ajax, MySql are used.