Docta Complutense :: Browsing by Author "Rodríguez, Juan Tinguaro"

Browsing by Author "Rodríguez, Juan Tinguaro"

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A bipolar knowledge representation model to improve supervised fuzzy classification algorithms.
(Springer-Verlag, 2018) Villarino, Guillermo; Gomez, Daniel; Rodríguez, Juan Tinguaro; Montero, Javier
Most supervised classification algorithms produce a soft score (either a probability, a fuzzy degree, a possibility, a cost, etc.) assessing the strength of the association between items and classes. After that, each item is assigned to the class with the highest soft score. In this paper, we show that this last step can be improved through alternative procedures more sensible to the available soft information. To this aim, we propose a general fuzzy bipolar approach that enables learning how to take advantage of the soft information provided by many classification algorithms in order to enhance the generalization power and accuracy of the classifiers. To show the suitability of the proposed approach, we also present some computational experiences for binary classification problems, in which its application to some well-known classifiers as random forest, classification trees and neural networks produces a statistically significant improvement in the performance of the classifiers.
A Computational Definition of Aggregation Rules.
(IEEE, 2010) Rodríguez, Juan Tinguaro; López, V.; Gómez, D.; Vitoriano, Begoña; Montero, Javier
The currently-in-use definition of aggregation function is analyzed in this paper, noting that the introduced variability in the dimension of information does not avoid some obvious dysfunctions. In particular, a potential abuse of the mathematical formalism underlies such a definition, which could lead to solve a complex concept by means of a formal mathematical expression. In this paper we propose an alternative definition making emphasis on the practical implementation of aggregation functions, taking into account the objectives and limitations observed in the application of aggregation functions within the fuzzy context.
A decision support tool for humanitarian operations in natural disaster relief
(World Scientific, 2008) Rodríguez, Juan Tinguaro; Vitoriano, Begoña; Montero, Javier; Omaña, Antonio; Ruan, Da; Montero, Javier
In this paper we present a decision support system for primary action of international organizations devoted to natural disaster relief In particular, we pretend to build up an expert system that taking into account past experiences will help decision makers, mainly non-governmental organizations, to start or not an operation, depending on the place and the very first information about a possible natural disaster. The relevance of this issue is extreme, since such a decision must be taken as soon as possible.
A disaster-severity assessment DSS comparative analysis
(Springer, 2011) Rodríguez, Juan Tinguaro; Vitoriano, Begoña; Montero, Javier; Kecman, Vojislav
This paper aims to provide a comparative analysis of fuzzy rule-based systems and some standard statistical and other machine learning techniques in the context of the development of a decision support system (DSS) for the assessment of the severity of natural disasters. This DSS, whichwill be referred to as SEDD, has been proposed by the authors to help decision makers inside those Non-Governmental Organizations (NGOs) concerned with the design and implementation of international operations of humanitarian response to disasters. SEDD enables a relatively highly accurate and interpretable assessment on the consequences of almost every potential disaster scenario to be obtained through a set of easily accessible information about that disaster scenario and historical data about similar ones. Thus, although SEDD’s methodology is rather sophisticated, its data requirements are small, which, therefore, enables its use in the context of NGOs and countries requiring humanitarian aid. In this sense, SEDD opposes to some current tools which focuses on one phenomena-one place disaster scenarios (earthquakes in California, hurricanes in Florida, etc.) and/or have extensive and/or technologically sophisticated data requirements (real-time remote sensing information, exhaustive building census, etc.).Moreover, although focused on disaster response, SEDD can also be useful in other phases of disaster management, as disaster mitigation or preparedness. Particularly, the predictive accuracy and interpretability of SEDD fuzzy methodology is compared here in a disaster severity assessment context with those of multiple linear regression, linear discriminant analysis, classification trees and support vector machines. After an extensive validation over the EM-DAT disaster database, it is concluded that SEDD outperforms the methods above in the task of simultaneously providing an accurate and interpretable inference tool for the evaluation of the consequences of disasters.
A fuzzy and bipolar approach to preference modeling with application to need and desire
(Elsevier Science Bv, 2013-03-01) Franco, C.; Montero, Javier; Rodríguez, Juan Tinguaro
Fuzziness and bipolarity allow representing human knowledge, taking into account the gradual and the dialectic properties of language, focusing on the meaning of concepts. Under this cognitive and linguistic approach, we explore preference relations, examining their semantic decomposition through fuzzy preference structures and the specification of meaningful opposites. In particular, we introduce the Preference–Aversion (P–A) model, which allows analyzing, under an independent aggregation methodology, the possible gains and losses, like pros and cons, towards a given set of alternatives. As an attractive feature of this proposal, we show that the P–A model allows distinguishing between need and desire, contrary to common preference models where both notions are indistinguishable.
A fuzzy edge-based image segmentation approach
(Atlantis Press, 2015) Guada, Carely; Gómez, Daniel; Rodríguez, Juan Tinguaro; Yáñez, Javier; Montero, Javier
In this paper, we present a way to define the concept of fuzzy image segmentation, which has not been clearly defined in the literature. In this work the term Fuzzy image segmentation is characterized by means of a fuzzy set over the set of edges, which can be understood as the fuzzy boundary of the image. Also we discuss a visualization of an image segmentation in terms of edge detection. But first, we define two concepts of crisp image segmentation on an image network, one based on nodes and the other one focusing on edges.
A general methodology for data-based rule building and its application to natural disaster management.
(Pergamon-Elsevier Science Ltd, 2012) Rodríguez, Juan Tinguaro; Vitoriano, Begoña; Montero, Javier
Risks derived from natural disasters have a deeper impact than the sole damage suffered by the affected zone and its population. Because disasters can affect geostrategic stability and international safety, developed countries invest a huge amount of funds to manage these risks. A large portion of these funds are channeled through United Nations agencies and international non-governmental organizations (NGOs), which at the same time are carrying out more and more complex operations. For these reasons, technological support for these actors is required, all the more so because the global economic crisis is placing emphasis on the need for efficiency and transparency in the management of (relatively limited) funds. Nevertheless, currently available sophisticated tools for disaster management do not fit well into these contexts because their infrastructure requirements usually exceed the capabilities of such organizations. In this paper, a general methodology for inductive rule building is described and applied to natural-disaster management. The application is a data-based, two-level knowledge decision support system (DSS) prototype which provides damage assessment for multiple disaster scenarios to support humanitarian NGOs involved in response to natural disasters. A validation process is carried out to measure the accuracy of both the methodology and the DSS
A methodology for building fuzzy rules from data
(Public University of Navarre, 2009) Rodríguez, Juan Tinguaro; Lopez, Victoria; Montero, Javier; Vitoriano, Begoña; Burillo, P.; Bustince, H.; De Baets, B.; Fodor, J.
Extraction of rules for classification and decision tasks from databases is an issue of growing importance as automated processes based on data are being required in these fields. Interpretability of rules is improved by defining classes for independent variables. Moreover, though more complex, a more realistic and flexible framework is attained when fuzzy classes are considered. In this paper, an inductive approach is taken in order to develop a general methodology for building fuzzy rules from databases. Three types of rules are built in order to be able of dealing with both categorical and numerical data.
A methodology for hierarchical image segmentation evaluation
(Springer, 2016) Rodríguez, Juan Tinguaro; Guada, C.; Gomez, D.; Yáñez, Javier; Montero, Javier; Carvalho, Joao Paulo; Lesot, Marie-Jeanne; Kaymak, Uzay; Vieira, Susana; Bouchon-Meunier, Bernadette; Yager, Ronald R.
This paper proposes a method to evaluate hierarchical image segmentation procedures, in order to enable comparisons between different hierarchical algorithms and of these with other (non-hierarchical) segmentation techniques (as well as with edge detectors) to be made. The proposed method builds up on the edge-based segmentation evaluation approach by considering a set of reference human segmentations as a sample drawn from the population of different levels of detail that may be used in segmenting an image. Our main point is that, since a hierarchical sequence of segmentations approximates such population, those segmentations in the sequence that best capture each human segmentation level of detail should provide the basis for the evaluation of the hierarchical sequence as a whole. A small computational experiment is carried out to show the feasibility of our approach.
A natural-disaster management DSS for Humanitarian Non-Governmental Organisations
(Elservier, 2010) Rodríguez, Juan Tinguaro; Vitoriano, Begoña; Montero, Javier
Humanitarian Non-Governmental Organisations (NGOs) play a growing role in the response to natural disasters, but despite being largely demanded, there is no available decision support system (DSS) specifically designed to address their problem. In this paper we present a decision support system (DSS) to aid those Humanitarian NGOs concerned with the response to natural disasters. Such a DSS has been designed avoiding sophisticated methodologies that may exceed the infrastructural requirements and constraints of emergency management by NGOs. A data-based, two-level knowledge methodology which allows damage assessment of multiple disaster scenarios is presented in order to address that problem. Validation results show viability of our approach.
A New concept of of fuzzy image segmentation
(World Scientific Publishing Company, 2014) Gomez, D.; Zarrazola, E.; Yáñez, Javier; Rodríguez, Juan Tinguaro; Montero, Javier; De Moraes, Ronei Marcos; Kerre, Etienne E; Machado, Liliane dos Santos; Lu, Jie
A crisp image segmentation can be characterized in terms of the set of edges that separates the adjacent regions of the segmentation. Based on these edges, an alternative way to define a fuzzy image segmentation is introduced in this paper. In this sense, the notion of fuzzy image segmentation is characterized by means of a fuzzy set over the set of edges, which could in this way be understood as the fuzzy boundary of the image. Also, an algorithm to construct this fuzzy boundary is provided based on the relations that exist
A new modularity measure for Fuzzy Community detection problems based on overlap and grouping functions
(Elsevier Science INC, 2016) Gomez, D.; Rodríguez, Juan Tinguaro; Yáñez, Javier; Montero, Javier
One of the main challenges of fuzzy community detection problems is to be able to measure the quality of a fuzzy partition. In this paper, we present an alternative way of measuring the quality of a fuzzy community detection output based on n-dimensional grouping and overlap functions. Moreover, the proposed modularity measure generalizes the classical Girvan–Newman (GN) modularity for crisp community detection problems and also for crisp overlapping community detection problems. Therefore, it can be used to compare partitions of different nature (i.e. those composed of classical, overlapping and fuzzy communities). Particularly, as is usually done with the GN modularity, the proposed measure may be used to identify the optimal number of communities to be obtained by any network clustering algorithm in a given network. We illustrate this usage by adapting in this way a well-known algorithm for fuzzy community detection problems, extending it to also deal with overlapping community detection problems and produce a ranking of the overlapping nodes. Some computational experiments show the feasibility of the proposed approach to modularity measures through n-dimensional overlap and grouping functions.
A new view on the relationships between interval valured and intuitionistic fuzzy sets
(World Scientific, 2016) Franco, Camilo; Rodríguez, Juan Tinguaro; Montero, Javier; Gomez, D.; Zeng, Xianyi; Lu, Jie; Kerre, Etienne E.; Martinez, L.; Koehl, Ludovic
This paper proposes a novel approach to analyze the relationship between interval valued fuzzy sets (IVFS) and Atanassov’s intuitionistic fuzzy sets (AIFS), based on the recently introduced notion of paired structure. It is suggested that the different semantics of IVFS and AIFS, with respect to their particular purposes as extensions of fuzzy sets, can be formally recovered and separated in the framework of paired structures, pointing out that both models actually provide a quite different representational performance.
Aggregation tools for the evaluation of classifications
(IEEE, 2017) Castiblanco, Fabian; Gomez, D.; Montero, Javier; Rodríguez, Juan Tinguaro
This paper focuses on key issues related to aggregation tools within knowledge acquisition, and in particular for image processing. In particular, it is claimed the need of a number of simultaneous indices in order to evaluate the quality of a fuzzy classification (e.g., overlapping, covering and relevance), and that such a family of indices should allow learning, giving a hint on how such a classification can be improved. Our guess is that those indices can be obtained from certain families of well-known aggregation tools.
An axiomatic approach to the notion of semantic antagonism
(Society for soft computing, 2011) Rodríguez, Juan Tinguaro; Franco, Camilo; Vitoriano, Begoña; Montero, Javier; Hiroto, K.; Mukaidono, M.; Kuswadi, S.
The concept of semantic antagonism refers to the human capability of characterizing those objects of an universe of discourse being dissimilar, significantly different from or opposite to a given concept, predicate or previous knowledge. This capability is essential in the formation of linguistic polarities, such as false/true or good/bad, that enable us to analyze, organize (classify) and give meaning to reality in terms of opposite poles of semantic reference. Though they are related, the notion of semantic antagonism is somehow more general than that of antonymy, since the former allows to characterize opposition even in the absence of antonym words and is not constrained by the assumption of symmetry that underlies the last. Therefore, the notion of semantic antagonism seems to be well suited for giving base to those knowledge representation frameworks which introduce some kind of bipolarity or distinction between positive and negative information. In this paper, an axiomatic approach is taken in order to describe the reasonable assumptions a dissimilarity operator acting on a set of predicates should obey. This enables to derive a basic differentiation of these operators, and particularly to show that antonyms are special cases of antagonistic predicates. Furthermore, through the proposed axioms it is possible to introduce the idea of dissimilarity structure over a set of predicates, and the application of this last notion in the context of supervised learning for classification tasks is briefly described.
An Inductive Methodology for Data-Based Rules Building
(Springer-Verlag Berlin Heidelberg, 2009) Rodríguez, Juan Tinguaro; Montero, Javier; Vitoriano, Begoña; Lopez, Victoria; Rossi, Francesca; Tsoukias, Alexis
Extraction of rules from databases for classification and decision tasks is an issue of growing importance as automated processes based on data are being required in these fields. An inductive methodology for data-based rules building and automated learning is presented in this paper. A fuzzy framework is used for knowledge representation and, through the introduction and the use of dual properties in the valuation space of response variables, reasons for and against the rules are evaluated from data. This make possible to use continuous DDT logic, which provides a more general and informative framework, in order to assess the validity of rules and build an appropriate knowledge base.
An ordinal approach to computing with words and the preference–aversion model
(Elsevier, 2014) Franco de los Ríos, Camilo A.; Rodríguez, Juan Tinguaro; Montero, Javier
Computing with words (CWW) explores the brain’s ability to handle and evaluate perceptions through language, i.e., by means of the linguistic representation of information and knowledge. On the other hand, standard preference structures examine decision problems through the decomposition of the preference predicate into the simpler situations of strict preference, indifference and incomparability. Hence, following the distinctive cognitive/ neurological features for perceiving positive and negative stimuli in separate regions of the brain, we consider two separate and opposite poles of preference and aversion, and obtain an extended preference structure named the Preference–aversion (P–A) structure. In this way, examining the meaning of words under an ordinal scale and using CWW’s methodology, we are able to formulate the P–A model under a simple and purely linguistic approach to decision making, obtaining a solution based on the preference and nonaversion order.
Another paraconsistent algebraic semantics for Lukasiewicz-Pavelka logic
(Elsevier, 2014-05-01) Rodríguez, Juan Tinguaro; Turunen, Esko; Ruan, Da; Montero, Javier
As recently proved in a previous work of Turunen, Tsoukias and Ozttirk, starting from an evidence pair (a, h) on the real unit square and associated with a propositional statement a, we can construct evidence matrices expressed in terms of four values t, f, k, u that respectively represent the logical valuations true, false, contradict ion (both true and false) and unknown (neither true nor false) regarding the statement a. The components of the evidence pair (a, h) are to be understood as evidence for and against a, respectively. Moreover, the set of all evidence matrices can be equipped with an injective MV-algebra structure. Thus, the set of evidence matrices can play the role of truth-values of a Lukasiewicz Pavelka fuzzy logic, a rich and applicable mathematical foundation for fuzzy reasoning, and in such a way that the obtained new logic is paraconsistent. In this paper we show that a similar result can be also obtained when the evidence pair (a, h) is given on the real unit triangle. Since the real unit triangle does not admit a natural MV-structure, we introduce some mathematical results to show how this shortcoming can be overcome, and another injective MV-algebra structure in the corresponding set of evidence matrices is obtained. Also, we derive several formulas to explicitly calculate the evidence matrices for the operations associated to the usual connectives.
Approaches to learning strictly-stable weights for data with missing values.
(Elsevier Science Bv, 2017) Beliakov, G.; Gomez, D.; Jameson, Simon S; Montero, Javier; Rodríguez, Juan Tinguaro
The problem of missing data is common in real-world applications of supervised machine learning such as classification and regression. Such data often gives rise to the need for functions defined for varying dimension. Here we propose optimization methods for learning the weights of quasi-arithmetic means in the context of data with missing values. We investigate some alternative approaches depending on the number of variables that have missing values and show results for several numerical experiments.
Bipolarity in social sciences and mathematics
(World Scientific Publishing Company, 2014) Franco, Camilo; Rodríguez, Juan Tinguaro; Montero, Javier; De Moraes, Roine Marcos; Kerre, Etienne E.; Machado, Liliane dos Santos; Lu, Jie
The polarity of concepts and the dialectic process by which its meaning emerges has been subject of interest since the ancient Greeks. Recently, the term Bipolarity has been used in social and mathematical sciences, referring to the measurement of the meaning of concepts. It is claimed that the measuring process has to consider at least an associated pair of meaningful opposites, such that some type of structure is used to analyze the aspect of reality that is being modeled. From this point of view, we take a quick overview on the genealogy of Bipolarity, discussing some ideas about the nature of negative knowledge, and how it has been examined recently, and not so recently, by the mathematical community.

Browsing by Author "Rodríguez, Juan Tinguaro"

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