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Torra V.,Artificial Intelligence Research Institute
International Journal of Intelligent Systems | Year: 2010

Several extensions and generalizations of fuzzy sets have been introduced in the literature, for example, Atanassov's intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets. In this paper, we propose hesitant fuzzy sets. Although from a formal point of view, they can be seen as fuzzy multisets, we will show that their interpretation differs from the two existing approaches for fuzzy multisets. Because of this, together with their definition, we also introduce some basic operations. In addition, we also study their relationship with intuitionistic fuzzy sets. We prove that the envelope of the hesitant fuzzy sets is an intuitionistic fuzzy set. We prove also that the operations we propose are consistent with the ones of intuitionistic fuzzy sets when applied to the envelope of the hesitant fuzzy sets.© 2010 Wiley Periodicals, Inc.

Navarro-Arribas G.,Autonomous University of Barcelona | Torra V.,Artificial Intelligence Research Institute
Information Fusion | Year: 2012

In this paper, we review the role of information fusion in data privacy. To that end, we introduce data privacy, and describe how information and data fusion are used in some fields of data privacy. Our study is focused on the use of aggregation for privacy protections, and record linkage techniques. © 2011 Elsevier B.V. All rights reserved.

Zaidi N.A.,Monash University | Cerquides J.,Artificial Intelligence Research Institute | Carman M.J.,Monash University | Webb G.I.,Monash University
Journal of Machine Learning Research | Year: 2013

Despite the simplicity of the Naive Bayes classifier, it has continued to perform well against more sophisticated newcomers and has remained, therefore, of great interest to the machine learning community. Of numerous approaches to refining the naive Bayes classifier, attribute weighting has received less attention than it warrants. Most approaches, perhaps influenced by attribute weighting in other machine learning algorithms, use weighting to place more emphasis on highly predictive attributes than those that are less predictive. In this paper, we argue that for naive Bayes attribute weighting should instead be used to alleviate the conditional independence assumption. Based on this premise, we propose a weighted naive Bayes algorithm, called WANBIA, that selects weights to minimize either the negative conditional log likelihood or the mean squared error objective functions. We perform extensive evaluations and find that WANBIA is a competitive alternative to state of the art classifiers like Random Forest, Logistic Regression and A1DE. © 2013 Nayyar A. Zaidi, Jesus Cerquides, Mark J. Carman and Geoffrey I. Webb.

Schorlemmer M.,Artificial Intelligence Research Institute | Robertson D.,University of Edinburgh
IEEE Transactions on Knowledge and Data Engineering | Year: 2011

We address the problem of how to reason about properties of knowledge transformations as they occur in distributed and decentralized interactions between large and complex artifacts, such as databases, web services, and ontologies. Based on the conceptual distinction between specifications of interactions and properties of knowledge transformations that follow from these interactions, we explore a novel mixture of process calculus and property inference by connecting interaction models with knowledge transformation rules. We aim at being generic in our exploration, hence our emphasis on abstract knowledge transformations, although we exemplify it using a lightweight specification language for interaction modeling (for which an executable peer-to-peer environment already exists) and provide a formal semantics for knowledge transformation rules using the theory of institutions. Consequently, our exploration is also an example of the gain obtained by linking current state-of-the-art distributed knowledge engineering based on web services and peer-based architectures with formal methods drawn from a long tradition in algebraic specification. © 2006 IEEE.

Atencia M.,French Institute for Research in Computer Science and Automation | Atencia M.,Joseph Fourier University | Schorlemmer M.,Artificial Intelligence Research Institute | Schorlemmer M.,University of Barcelona
Journal of Web Semantics | Year: 2012

We tackle the problem of semantic heterogeneity in the context of agent communication and argue that solutions based solely on ontologies and ontology matching do not capture adequately the richness of semantics as it arises in dynamic and open multiagent systems. Current solutions to the semantic heterogeneity problem in distributed systems usually do not address the contextual nuances of the interaction underlying an agent communication. The meaning an agent attaches to its utterances is, in our view, very relative to the particular dialogue in which it may be engaged, and that the interaction model specifying its dialogical structure and its unfolding should not be left out of the semantic alignment mechanism. In this article we provide the formal foundation of a novel, interaction-based approach to semantic alignment, drawing from a mathematical construct inspired from category theory that we call the communication product. In addition, we describe a simple alignment protocol which, combined with a probabilistic matching mechanism, endows an agent with the capacity of bootstrapping - by repeated successful interaction - the basic semantic relationship between its local vocabulary and that of another agent. We have also implemented the alignment technique based on this approach and prove its viability by means of an abstract experimentation and a thorough statistical analysis. © 2011 Elsevier B.V. All rights reserved.

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