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URI permanente para esta colecciónhttps://hdl.handle.net/10953/218

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Using Linguistic Incomplete Preference Relations to Cold start Recommendations
(Emerald, 2010-06) Rodríguez, Rosa M.; Espinilla, Macarena; Sánchez, Pedro J.; Martínez, Luis
Purpose – Analyzing current recommender systems, it is observed that the cold start problem is still too far away to be satisfactorily solved. This paper aims to present a hybrid recommender system which uses a knowledge-based recommendation model to provide good cold start recommendations. Design/methodology/approach – Hybridizing a collaborative system and a knowledge-based system, which uses incomplete preference relations means that the cold start problem is solved. The management of customers’ preferences, necessities and perceptions implies uncertainty. To manage such an uncertainty, this information has been modeled by means of the fuzzy linguistic approach. Findings – The use of linguistic information provides flexibility, usability and facilitates the management of uncertainty in the computation of recommendations, and the use of incomplete preference relations in knowledge-based recommender systems improves the performance in those situations when collaborative models do not work properly. Research limitations/implications – Collaborative recommender systems have been successfully applied in many situations, but when the information is scarce such systems do not provide good recommendations. Practical implications – A linguistic hybrid recommendation model to solve the cold start problem and provide good recommendations in any situation is presented and then applied to a recommender system for restaurants. Originality/value – Current recommender systems have limitations in providing successful recommendations mainly related to information scarcity, such as the cold start. The use of incomplete preference relations can improve these limitations, providing successful results in such situations.
Optimization algorithm for learning consistent belief rule-base from examples
(Springer Link, 2011-10) Liu, Jun; Martínez, Luis; Ruan, Da; Rodríguez, Rosa M.; Calzada, Alberto
A belief rule-based inference approach and its corresponding optimization algorithm deal with a rule-base with a belief structure called a belief rule base (BRB) that forms a basis in the inference mechanism. In this paper, a new learning method is proposed based on the given sample data for optimally generating a consistent BRB. The focus is given on the consistency of BRB knowing that the consistency conditions are often violated if the system is generated from real world data. The measurement of BRB inconsistency is incorporated in the objective function of the optimization algorithm. This process is formulated as a non-linear constraint optimization problem and solved using the optimization tool provided in MATLAB. A numerical example is demonstrated the effectiveness of the proposed algorithm.
An Analysis of Symbolic Linguistic Computing Models in Decision Making
(Taylor and Francis, 2012-07) Rodríguez, Rosa M.; Martínez, Luis
It is common that experts involved in complex real-world decision problems use natural language for expressing their knowledge in uncertain frameworks. The language is inherent vague, hence probabilistic decision models are not very suitable in such cases. Therefore, other tools such as fuzzy logic and fuzzy linguistic approaches have been successfully used to model and manage such vagueness. The use of linguistic information implies to operate with such a type of information, i.e. processes of computing with words (CWW). Different schemes have been proposed to deal with those processes, and diverse symbolic linguistic computing models have been introduced to accomplish the linguistic computations. In this paper, we overview the relationship between decision making and CWW, and focus on symbolic linguistic computing models that have been widely used in linguistic decision making to analyse if all of them can be considered inside of the CWW paradigm.
An attitude-driven web consensus support system for heterogeneous group decision making
(Elsevier, 2013-01) Palomares, Ivan; Rodriguez, Rosa M.; Martinez, Luis
Consensus reaching processes are applied in group decision making problems to reach a mutual agreement among a group of decision makers before making a common decision. Different consensus models have been developed to facilitate consensus reaching processes. However, new trends bring diverse challenges in group decision making, such as the modelling of different types of information and of large groups of decision makers, together with their attitude to achieve agreements. These challenges require the capacity to deal with heterogenous frameworks, and the automation of consensus reaching processes by means of consensus support systems. In this paper, we propose a consensus model in which decision makers can express their opinions by using different types of information, capable of dealing with large groups of decision makers. The model incorporates the management of the group’s attitude towards consensus by means of an extension of OWA aggregation operators aimed to optimize the overall consensus process. Eventually, a novel Web-based consensus support system that automates the proposed consensus model is presented.
Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches
(Elsevier, 2013-08) Martín Valdivia, M. Teresa; Martínez Cámara, Eugenio; Perea Ortega, Jose M.; Ureña López, L. Alfonso
Sentiment polarity detection is one of the most popular tasks related to Opinion Mining. Many papers have been presented describing one of the two main approaches used to solve this problem. On the one hand, a supervised methodology uses machine learning algorithms when training data exist. On the other hand, an unsupervised method based on a semantic orientation is applied when linguistic resources are available. However, few studies combine the two approaches. In this paper we propose the use of meta-classifiers that combine supervised and unsupervised learning in order to develop a polarity classification system. We have used a Spanish corpus of film reviews along with its parallel corpus translated into English. Firstly, we generate two individual models using these two corpora and applying machine learning algorithms. Secondly, we integrate SentiWordNet into the English corpus, generating a new unsupervised model. Finally, the three systems are combined using a meta-classifier that allows us to apply several combination algorithms such as voting system or stacking. The results obtained outperform those obtained using the systems individually and show that this approach could be considered a good strategy for polarity classification when we work with parallel corpora.
A Group Decision Making Model Dealing with Comparative Linguistic Expressions based on Hesitant Fuzzy Linguistic Term Sets
(Elsevier, 2013-08) Rodríguez, Rosa M.; Martínez, Luis; Herrera, Francisco
The complexity and impact of many real world decision making problems lead to the necessity of considering multiple points of view, building group decision making problems in which a group of experts provide their preferences to achieve a solution. In such complex problems uncertainty is often present and although the use of linguistic information has provided successful results in managing it, these are sometimes limited because the linguistic models use single-valued and predefined terms that restrict the richness of freely eliciting the preferences of the experts. Usually, experts may doubt between different linguistic terms and require richer expressions to express their knowledge more accurately. However, linguistic group decision making approaches do not provide any model to make more flexible the elicitation of linguistic preferences in such hesitant situations. In this paper is proposed a new linguistic group decision model that facilitates the elicitation of flexible and rich linguistic expressions, in particular through the use of comparative linguistic expressions, close to human beings’ cognitive models for expressing linguistic preferences based on hesitant fuzzy linguistic term sets and context-free grammars. This model defines the group decision process and the necessary operators and tools to manage such linguistic expressions.
A Knowledge based Approach for Polarity Classification in Twitter
(Wiley, 2014-02) Montejo Ráez, Arturo; Martínez Cámara, Eugenio; Martín Valdivia, M. Teresa; Ureña López, L. Alfonso
Until now, most of the methods published for polarity classification in Twitter have used a supervised approach. The differences between them are only the features selected and the method used for weighting them. In this article, we present an unsupervised method for polarity classification in Twitter. The method is based on the expansion of the concepts expressed in the tweets through the application of PageRank to WordNet. In addition, we integrate SentiWordNet to compute the final value of polarity. The synsets values are weighted with the PageRank scores obtained in the previous random walk process over WordNet. The results obtained show that disambiguation and expansion are good strategies for improving overall performance.
A Fuzzy Envelope for Hesitant Fuzzy Linguistic Term Set and Its Application to Multicriteria Decision Making
(Elsevier, 2014-02) Liu, Hongbin; Rodriguez, Rosa M.
Decision making is a process common to human beings. The uncertainty and fuzziness of problems demand the use of the fuzzy linguistic approach to model qualitative aspects of problems related to decision. The recent proposal of hesitant fuzzy linguistic term sets supports the elicitation of comparative linguistic expressions in hesitant situations when experts hesitate among different linguistic terms to provide their assessments. The use of linguistic intervals whose results lose their initial fuzzy representation was introduced to facilitate the computing processes in which such expressions are used. The aim of this paper is to present a new representation of the hesitant fuzzy linguistic term sets by means of a fuzzy envelope to carry out the computing with words processes. This new fuzzy envelope can be directly applied to fuzzy multicriteria decision making models. An illustrative example of its application to a supplier selection problem through the use of fuzzy TOPSIS is presented.
Hesitant Fuzzy Sets: State of the Art and Future Directions
(Wiley, 2014-04) Rodríguez, Rosa M.; Martínez, Luis; Torra, Vicenç; Xu, Zeshui; Herrera, Francisco
The necessity of dealing with uncertainty in real world problems has been a long-term research challenge that has originated different methodologies and theories. Fuzzy sets along with their extensions, such as type-2 fuzzy sets, interval-valued fuzzy sets, and Atanassov’s intuitionistic fuzzy sets, have provided a wide range of tools that are able to deal with uncertainty in different types of problems. Recently, a new extension of fuzzy sets so-called hesitant fuzzy sets has been introduced to deal with hesitant situations, which were not well managed by the previous tools. Hesitant fuzzy sets have attracted very quickly the attention of many researchers that have proposed diverse extensions, several types of operators to compute with such types of information, and eventually some applications have been developed. Because of such a growth, this paper presents an overview on hesitant fuzzy sets with the aim of providing a clear perspective on the different concepts, tools and trends related to this extension of fuzzy sets.
Integrating Spanish lexical resources by meta-classifiers for polarity classification
(SAGE PUBLICATIONS LTD, 2014-05-19) Martínez Cámara, Eugenio; Martín Valdivia, M. Teresa; M. Dolores, Molina González; José M., Perea Ortega
In this paper we focus on unsupervised sentiment analysis in Spanish. The lack of resources for languages other than English, as for example Spanish, adds more complexity to the task. However, we take advantage of some good already existing lexical resources. We have carried out several experiments using different unsupervised approaches in order to compare the different methodologies for solving the problem of the Spanish polarity classification in a corpus of movie reviews. Among all these approaches, perhaps the newest one integrates SentiWordNet with the Multilingual Central Repository to tackle polarity detection directly over the Spanish corpus. However, the results obtained were not as promising as we expected, and so we carried out another group of experiments combining all the methods using meta-classifiers. The results obtained with stacking outperformed the individual experiments and encourage us to continue in this way.
A Consensus-Driven Group Recommender System
(Wiley Periodicals, Inc, 2015) Castro, Jorge; Quesada, Francisco J.; Palomares, Iván; Martínez, Luis
Recommender systems aim at filtering large amounts of information for users, providing them with those pieces of information which better meet their preferences or needs. Such systems have been traditionally used in diverse areas, such as e-commerce or tourism. Within this context, group recommender systems address the problem of generating recommendations for groups of users who might have different interests. Although different aggregation processes have been extensively utilized in real-life applications to generate group recommendations, such processes do not guarantee that the list of products recommended to the group reflect a high agreement level among its members' individual preferences. Given the need for considering the added value of obtaining group recommendations under a high agreement level, this paper presents a novel group recommender system methodology that attempts to reach a high level of consensus among individual recommendations of group members. To do this, and inspired by existing group decision-making approaches in the literature, a consensus reaching process is carried out to bring such individual recommendations closer to each other before delivering the group recommendations.
A Spanish Semantic Orientation Approach to Domain Adaptation for Polarity Classification
(Elsevier, 2015-07) Molina González, M. Dolores; Martínez Cámara, Eugenio; Martín Valdivia, M. Teresa; Ureña López, L. Alfonso
One of the problems of opinion mining is the domain adaptation of the sentiment classifiers. There are several approaches to tackling this problem. One of these is the integration of a list of opinion bearing words for the specific domain. This paper presents the generation of several resources for domain adaptation to polarity detection. On the other hand, the lack of resources in languages different from English has orientated our work towards developing sentiment lexicons for polarity classifiers in Spanish. The results show the validity of the new sentiment lexicons, which can be used as part of a polarity classifier.
Language technologies applied to document simplification for helping autistic people
(Elsevier, 2015-07-15) Barbu, Eduard; Martín-Valdivia, María Teresa; Martínez-Cámara, Eugenio; Ureña-López, Alfonso
People affected by Autism Spectrum Disorders (ASD) have impairments in social interaction because they lack an adequate theory of mind. A significant percentile has inadequate reading comprehension skills. We present a multilingual tool called Open Book (OB) that applies Human Language Technologies (HLT) in order to identify reading comprehension obstacles in text documents and propose more simple alternatives with the aim of assisting the reading comprehension of users. OB involves several text transformations at lexical, syntactic and semantic level. In this paper we focus on three challenging components of the OB tool: the image retrieval component, the idiom detection component and the summarization module. There are very few studies that involve simplification by showing images associated to difficult concepts. In addition, the treatment of figurative language such as idioms or metaphors is one of the most challenging areas in Natural Language Processing (NLP). Finally, although text summarization is a more widely studied field in NLP, its application to text simplification remains as an open research issue. Thus, we focus on the integration of these three modules in our OB tool. We present the motivation for building these components and we describe how they are integrated in the whole system. Moreover, the usability and the usefulness of OB have been evaluated and analysed showing that the tool helps to produce texts easier to understand for autistic people.
Managing experts behavior in large-scale consensus reaching processes with uninorm aggregation operators
(Elsevier, 2015-10) Quesada, Francisco J.; Palomares, Iván; Martínez, Luis
In many real-life large scale group decision making problems, it can be necessary and convenient a consensus reaching process, which is an iterative procedure aimed at seeking a high degree of agreement amongst experts’ preferences before making a group decision. Although a wide variety of models and approaches have been proposed and developed to support consensus reaching processes, in large groups there are some important aspects that still require further study, such as the treatment of experts’ behaviors that could hamper reaching the wanted agreement. More specifically, it would be necessary an approach to deal with experts properly, based on the overall behavior they present during the discussion process, as well as reinforcing repeated patterns of cooperative (or uncooperative) behavior adopted by experts. This paper presents an expert weighting methodology for consensus reaching processes in large-scale group decision making, that incorporates the use of uninorm aggregation operators. Such operators, which are characterized by their property of full reinforcement, are used in the proposed methodology to allow the experts’ weighting based on their overall behavior during the consensus process and the behavior evolution across the time. This proposal is integrated in a consensus model for large-scale group decision making problems under uncertainty, and it is put in practice to show an illustrative example of its effectiveness and improvements with respect to other approaches.
Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making
(Elsevier, 2016-05) Xu, Yejun; Chen, Lei; Rodríguez, Rosa M.; Herrera, Francisco; Wang, Huimin
The concept of hesitant fuzzy preference relation (HFPR) has been recently introduced to allow the de- cision makers (DMs) to provide several possible preference values over two alternatives. This paper in- troduces a new type of fuzzy preference structure, called incomplete HFPRs, to describe hesitant and incomplete evaluation information in the group decision making (GDM) process. Furthermore, we define the concept of multiplicative consistency incomplete HFPR and additive consistency incomplete HFPR, and then propose two goal programming models to derive the priority weights from an incomplete HFPR based on multiplicative consistency and additive consistency respectively. These two goal programming models are also extended to obtain the collective priority vector of several incomplete HFPRs. Finally, a numerical example and a practical application in strategy initiatives are provided to illustrate the validity and applicability of the proposed models.
Incremental maintenance of discovered association rules and approximate dependencies
(IOS Press, 2017-01) Pérez, Alain; Blanco, Ignacio J.; González-González, Luisa M.; Serrano, José M.
Association Rules (ARs) and Approximate Dependencies (ADs) are significant fields in data mining and the focus of many research efforts. This knowledge, extracted by traditional mining algorithms becomes inexact when new data operations are executed, a common problem in real-world applications. Incremental mining methods arise to avoid re-runs of those algorithms from scratch by re-using information that is systematically maintained. These methods are useful to extract knowledge in dynamic environments. However, the implementation of algorithms only to maintain previously discovered information creates inefficiencies. In this paper, two active algorithms are proposed for incremental maintenance of previous discovered ARs and ADs, inspired by efficient computation of changes. These algorithms operate over a generic form of measures to efficiently maintain a wide range of rule metrics simultaneously. We also propose to compute data operations at real-time, in order to create a reduced relevant instance set. The algorithms presented do not discover new knowledge; they are just created to efficiently maintain previously extracted valuable information. Experimental results in real education data and repository datasets show that our methods achieve a good performance. In fact, they can significantly improve traditional mining, incremental mining, and a naïve approach.
Fuzzy intelligent system for patients with preeclampsia in wearable devices
(Wiley, 2017-10-12) Espinilla-Estévez, Macarena; Medina-Quero, Javier; García-Fernández, Ángel Luis; Campaña, Sixto; Londoño, Jorge
Preeclampsia affects from 5% to 14% of all pregnant women and is responsible for about 14% of maternal deaths per year in the world. This paper is focused on the use of a decision analysis tool for the early detection of preeclampsia in women at risk. This tool applies a fuzzy linguistic approach implemented in a wearable device. In order to develop this tool, a real dataset containing data of pregnant women with high risk of preeclampsia from a health center has been analyzed, and a fuzzy linguistic methodology with two main phases is used. Firstly, linguistic transformation is applied to the dataset to increase the interpretability and flexibility in the analysis of preeclampsia. Secondly, knowledge extraction is done by means of inferring rules using decision trees to classify the dataset. The obtained linguistic rules provide understandable monitoring of preeclampsia based on wearable applications and devices. Furthermore, this paper not only introduces the proposed methodology, but also presents a wearable application prototype which applies the rules inferred from the fuzzy decision tree to detect preeclampsia in women at risk. The proposed methodology and the developed wearable application can be easily adapted to other contexts such as diabetes or hypertension.
Web-based GIS application for real-time interaction of underground infrastructure through virtual reality
(ACM, 2017-11) Jurado, Juan M.; Graciano, Alejandro; Ortega, Lidia; Feito, Francisco R.
Real-time visualization in web-based system remains challenging due to the amount of information associated to a 3D urban models. However, these 3D models are not able to provide advanced management of urban infrastructures, such as underground facilities. Nowadays, 3D GIS is considered the appropriate tool to provide accurate analysis and decision support based on spatial data. This paper presents a web-GIS application for 3D visualization, navigation, interaction and analysis of underground infrastructures through virtual reality. The growth of underground cities is a complex problem without easy solutions. In general, these infrastructures cannot be directly visualized. Thus, subsoil mapping can help us to develop a clearer representation of underground's pipes, cables or water mains. In addition, the approach of virtual reality provides an immersive experience and novelty interaction to acquire a complete knowledge about underground city structures. Experimental results show an integral application for the efficient management of underground infrastructure in real-time.
Real-time visualization of 3D terrains and subsurface geological structures
(Elsevier, 2018-01) Graciano, Alejandro; Rueda Ruiz, Antonio Jesús; Feito, Francisco R.
Geological structures, both at the surface and subsurface levels, are typically represented by means of voxel data. This model presents a major drawback: its large storage requirements. In this paper, we address this problem and pro- pose the use of a stack-based representation for geological surface-subsurface structures. Although this representation has been mainly used for volumetric terrain visualization in previous works, it has been used as an auxiliary data structure. Therefore, our main contribution in this work is its use as a first-class representation for both processing and visualization of surface and subsurface in- formation. The proposed solution provides real-time visualization of volumetric terrains and subsurface geological structures represented as stacks using a com- pact data representation in the GPU. Different GPU memory implementations of the stacks have been described, discussing the tradeoffs between performance and storage efficiency. We also introduce a novel algorithm for the calculation of the surface normal vectors using a hybrid object-image space strategy. More- over, important features for geoscientific applications such as visualization of boreholes or geological cross sections, and selective attenuation of strata have also been implemented in a straightforward way.
Pictogram Tablet: A Speech Generating Device Focused on Language Learning
(Oxford Academic, 2018-01-27) Martínez-Santiago, Fernando; Montejo-Ráez, Arturo; García-Cumbreras, Miguel Á.
Speech generating devices (SGD) are a type of communication aid based on turning a message composed by the user into speech. The goal of these communicative aids is usually to find technological tools for communication problems rather than tools for scaffolding the learning process of language skills. We have implemented an interface designed for developing new communication skills rather than improving the act of communication. This tool is focused on augmentative and alternative communication methods. A number of interface design principles are proposed, which are based on some of the most well-known speech disorder interventions, such as analysis of verbal behavior or language acquisition modeling.

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