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A Behavioural Model For Linguistic Uncertainty
 INFORMATION SCIENCES
, 1998
"... The paper discusses the problem of modelling linguistic uncertainty, which is the uncertainty produced by statements in natural language. For example, the vague statement `Mary is young' produces uncertainty about Mary's age. We concentrate on simple affirmative statements of the type `subject is pr ..."
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Cited by 16 (3 self)
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The paper discusses the problem of modelling linguistic uncertainty, which is the uncertainty produced by statements in natural language. For example, the vague statement `Mary is young' produces uncertainty about Mary's age. We concentrate on simple affirmative statements of the type `subject is predicate', where the predicate satisfies a special condition called monotonicity. For this case, we model linguistic uncertainty in terms of upper probabilities, which are given a behavioural interpretation as betting rates. Possibility measures and probability measures are special types of upper probability measure. We evaluate Zadeh's suggestion that possibility measures should be used to model linguistic uncertainty and the Bayesian claim that probability measures should be used. Our main conclusion is that, when the predicate is monotonic, possibility measures are appropriate models for linguistic uncertainty. We also discuss several assessment strategies for constructing a numerical model.
Intervalvalued fuzzy sets, possibility theory and imprecise probability
 In Proceedings of International Conference in Fuzzy Logic and Technology
, 2005
"... Intervalvalued fuzzy sets were proposed thirty years ago as a natural extension of fuzzy sets. Many variants of these mathematical objects exist, under various names. One popular variant proposed by Atanassov starts by the specification of membership and nonmembership functions. This paper focuses ..."
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Cited by 13 (2 self)
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Intervalvalued fuzzy sets were proposed thirty years ago as a natural extension of fuzzy sets. Many variants of these mathematical objects exist, under various names. One popular variant proposed by Atanassov starts by the specification of membership and nonmembership functions. This paper focuses on interpretations of such extensions of fuzzy sets, whereby the two membership functions that define them can be justified in the scope of some information representation paradigm. It particularly focuses on a recent proposal by Neumaier, who proposes to use intervalvalued fuzzy sets under the name “clouds”, as an efficient method to represent a family of probabilities. We show the connection between clouds, intervalvalued fuzzy sets and possibility theory. Keywords: Intervalvalued fuzzy sets, possibility theory probability theory
Joint propagation of probability and possibility in risk analysis: Towards a formal framework
 INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
, 2007
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`Fuzzy' vs `Nonfuzzy' in Combining Classifiers Designed by Boosting
"... Boosting is recognized as one of the most successful techniques for generating classifier ensembles. Typically, the classifier outputs are combined by the weighted majority vote. The purpose of this study is to demonstrate the advantages of some fuzzy combination methods for ensembles of classifiers ..."
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Cited by 11 (0 self)
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Boosting is recognized as one of the most successful techniques for generating classifier ensembles. Typically, the classifier outputs are combined by the weighted majority vote. The purpose of this study is to demonstrate the advantages of some fuzzy combination methods for ensembles of classifiers designed by Boosting. We ran 2fold crossvalidation experiments on 6 benchmark data sets to compare the fuzzy and nonfuzzy combination methods. On the "fuzzy side" we used the fuzzy integral and the decision templates with different similarity measures. On the "nonfuzzy side" we tried simple combiners such as the majority vote, minimum, maximum, average, product, and the Naive Bayes combination. Surprisingly, the minimum, maximum, average and product, which have been reported elsewhere to work very well on a variety of problems, appeared to be inadequate for our task. Thus the real contest was among the fuzzy combination methods on the one hand, and the weighted majority vote, the simple majority vote, and the Naive Bayes combiner, on the other hand. In our experiments, the fuzzy methods performed consistently better than the nonfuzzy methods. The weighted majority vote showed a stable performance, though slightly inferior to the performance of the fuzzy combiners. The majority vote and the Naive Bayes combiners had erratic behavior, ranging from the best to the worst contestants for different data sets.
Computing with words in decision making: foundations, trends and prospects
, 2009
"... Computing with Words (CW) methodology has been used in several different environments to narrow the differences between human reasoning and computing. As Decision Making is a typical human mental process, it seems natural to apply the CW methodology in order to create and enrich decision models in w ..."
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Cited by 6 (1 self)
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Computing with Words (CW) methodology has been used in several different environments to narrow the differences between human reasoning and computing. As Decision Making is a typical human mental process, it seems natural to apply the CW methodology in order to create and enrich decision models in which the information that is provided and manipulated has a qualitative nature. In this paper we make a review of the developments of CW in decision making. We begin with an overview of the CW methodology and we explore different linguistic computational models that have been applied to the decision making field. Then we present an historical perspective of CW in decision making by examining the pioneer papers in the field along with its most recent applications. Finally, some current trends, open questions and prospects in the topic are pointed out.
Fuzzy Rules in CaseBased Reasoning
 in Conf. AFIA99 Raisonnement à Partir de Cas
, 1999
"... Similaritybased fuzzy rules are proposed as a basic tool for modelling and formalizing parts of the casebased reasoning methodology within the framework of approximate reasoning. The use of different types of rules for encoding the heuristic reasoning principle underlying casebased problem s ..."
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Cited by 4 (0 self)
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Similaritybased fuzzy rules are proposed as a basic tool for modelling and formalizing parts of the casebased reasoning methodology within the framework of approximate reasoning. The use of different types of rules for encoding the heuristic reasoning principle underlying casebased problem solving is discussed, which leads to different approaches to casebased inference. A model which combines a constraintbased and an exampleoriented approach is advocated more particularly. Besides, the use of modifiers in fuzzy rules is proposed for adapting the proposed formal model to the respective applications, and the problem of determining appropriate modifiers is considered in the context of casebased learning. 1 Introduction The guiding principle underlying most casebased reasoning (Cbr) systems is the "Cbr hypothesis" which, loosely spoken, assumes that "similar problems have similar solutions." More precisely, the idea of Cbr is to exploit the experience from similar ...
Coherent Functions in Autonomous Systems
, 2002
"... INTRODUCTION Advanced sensorimotor devices, like mobile robots, are often referred to as autonomous systems. The expression is used to intentionally remark on the difference between these systems and those of traditional industrial automation. Although no rigorous definition is easily available, a ..."
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Cited by 3 (2 self)
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INTRODUCTION Advanced sensorimotor devices, like mobile robots, are often referred to as autonomous systems. The expression is used to intentionally remark on the difference between these systems and those of traditional industrial automation. Although no rigorous definition is easily available, a general informal consensus seems to exist on the features that denote autonomy: a system is considered the more autonomous the more reliably it can survive and perform tasks in the real world, without the need for human intervention. 171 ____________________________ *email: claudio.sossai@isib.cnr.it J. of Mult.Valued Logic & Soft Computing., Vol. 9, pp. 171194 2003 Old City Publishing, Inc. Reprints available directly from the publisher Published by license under the OCP Science imprint, Photocopying permitted by license only a member of the Old City Publishing Group In the literature different ideas and techniques have been proposed and investigated to achieve these results, nonethe
Fusion of Symbolic Knowledge and Uncertain Information in Robotics
 International Journal of Intelligent Systems
, 2001
"... The interpretation of data coming from the real world may require different and often complementary uncertainty models: some are better described by possibility theory, others are intrinsically probabilistic. A logic for belief functions is introduced to axiomatize both semantics as special cases. A ..."
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Cited by 2 (1 self)
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The interpretation of data coming from the real world may require different and often complementary uncertainty models: some are better described by possibility theory, others are intrinsically probabilistic. A logic for belief functions is introduced to axiomatize both semantics as special cases. As it properly extends classical logic, it also allows the fusion of data with different semantics and symbolic knowledge. The approach has been applied to the problem of mobile robot localization. For each place in the environment, a set of logical propositions allows the system to calculate the belief of the robot's presence as a function of the partial evidences provided by the individual sensors.
Prototyping and Browsing Image Databases Using Linguistic Summaries
 Proc. of the IEEE Int. Conf. on Fuzzy Systems (FUZZIEEE’2002
, 2002
"... In this paper, a new approach for the summarization of image databases is described. A linguistic description of image is first generated from its low level features such as color. An original incremental summary process, called SAINTETIQ is then applied to the images, leading to a comprehensive set ..."
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Cited by 2 (1 self)
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In this paper, a new approach for the summarization of image databases is described. A linguistic description of image is first generated from its low level features such as color. An original incremental summary process, called SAINTETIQ is then applied to the images, leading to a comprehensive set of summaries of parts of the database, hierarchically organized. The summary process is driven by data and relies on background knowledge represented by a fuzzy relational thesaurus. It provides both a proximity measure between terms of color domain, and a way to produce generalized descriptions of summaries. Finally, this article points out usages of the produced summaries. A new method to browse image databases is then presented as well as a way to produce relevant prototypes from groups of images. Those tasks benefit from the linguistic descriptions of summaries provided by SAINTETIQ.
On various definitions of the variance of a fuzzy random variable
 5TH INTERNATIONAL SYMPOSIUM ON IMPRECISE PROBABILITY: THEORIES AND APPLICATIONS, PRAGUE, CZECH REPUBLIC
, 2007
"... According to the current literature, there are two different approaches to the definition of the variance of a fuzzy random variable. In the first one, the variance is defined as a fuzzy interval, offering a gradual description of our incomplete knowledge about the variance of an underlying, impreci ..."
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Cited by 2 (0 self)
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According to the current literature, there are two different approaches to the definition of the variance of a fuzzy random variable. In the first one, the variance is defined as a fuzzy interval, offering a gradual description of our incomplete knowledge about the variance of an underlying, imprecisely observed, classical random variable. In the second case, the variance of the fuzzy random variable is defined as a crisp number, that makes it easier to handle in further processing. In this work, we introduce yet another definition of the variance of a fuzzy random variable, in the context of the theory of imprecise probabilities. The new variance is not defined as a fuzzy or crisp number, but it is a real interval, which is a compromise between both previous definitions. Our main objectives are twofold: first, we show the interpretation of the new variance and, second, with the help of simple examples, we demonstrate the usefulness of all these definitions when applied to particular situations.