Luis M. de Campos

Scopus Publications

Combining content information with collaborative filtering for publication venue recommendation
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Knowledge and Information Systems, 2026
This paper addresses the problem of academic venue recommendation by developing a hybrid collaborative filtering model that integrates both behavioral and content information. We propose two complementary strategies for incorporating textual content into the collaborative filtering process: enriching the definition of neighborhoods and enhancing the computation of ratings. To evaluate these approaches, we conduct experiments on two document collections, PMSC-UGR and CORD-19, and benchmark them against two state-of-the-art baselines: a publication-based model, which constructs neighborhoods from authors’ venue rating vectors, and a coauthorship-based model, which relies on shared publications to establish similarity. In addition, we explore alternative neighborhood definitions that capture author similarity through textual features, enabling the derivation of latent venue preferences. Experimental results show that integrating content information consistently improves recommendation quality, either by refining neighborhoods or by adjusting ratings. The findings also highlight the importance of adapting the role of textual content to the characteristics of the dataset, as well as the need to investigate richer text representations to mitigate redundancy effects observed when combining content in both components of the model.
EVALUATING TOURIST DISSATISFACTION WITH ASPECT-BASED SENTIMENT ANALYSIS USING SOCIAL MEDIA DATA
Marlon Santiago Viñán-ludeña, Luis De Campos
Advances in Hospitality and Tourism Research, 2024
Tourism satisfaction is essential for encouraging tourists to stay longer, spend more and return. However, visitor dissatisfaction can also prove useful for understanding any shortcomings of a tourist destination, and Twitter, Instagram and TripAdvisor reviews might be able to provide an insight into tourist perceptions and experiences. This study examines the major causes of tourist dissatisfaction with a tourism destination using an aspect-based sentiment analysis approach to understand the key points of negative tweets, posts or reviews. We examined 19,340 tweets, 7,712 Instagram posts and 25,483 reviews about Granada in Spain in order to evaluate the negative user's perceptions, discover management-related problems and provide feedback to destination management organizations to enable them to improve their services and operations. Our work contributes to computational methods to address tourism (dis)satisfaction with a process to identify the most important entities (places), an algorithm to identify aspects and opinions, and the use of word-trees to show the most important aspect-opinion tuples. In practical terms, we provide to tourism industry professionals and managers, as well as travelers, with methods to identify the reasons for tourist dissatisfaction from available social media data, in such a way that managerial strategies or travel plans can be improved.
An explainable content-based approach for recommender systems: a case study in journal recommendation for paper submission
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
User Modeling and User Adapted Interaction, 2024
Explainable artificial intelligence is becoming increasingly important in new artificial intelligence developments since it enables users to understand and consequently trust system output. In the field of recommender systems, explanation is necessary not only for such understanding and trust but also because if users understand why the system is making certain suggestions, they are more likely to consume the recommended product. This paper proposes a novel approach for explaining content-based recommender systems by specifically focusing on publication venue recommendation. In this problem, the authors of a new research paper receive recommendations about possible journals (or other publication venues) to which they could submit their article based on content similarity, while the recommender system simultaneously explains its decisions. The proposed explanation ecosystem is based on various elements that support the explanation (topics, related articles, relevant terms, etc.) and is fully integrated with the underlying recommendation model. The proposed method is evaluated through a user study in the biomedical field, where transparency, satisfaction, trust, and scrutability are assessed. The obtained results suggest that the proposed approach is effective and useful for explaining the output of the recommender system to users.
Information Retrieval and Machine Learning Methods for Academic Expert Finding
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Francisco J. Ribadas-Pena, Néstor Bolaños
Algorithms, 2024
In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance.
Use of topical and temporal profiles and their hybridisation for content-based recommendation
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
User Modeling and User Adapted Interaction, 2023
In the context of content-based recommender systems, the aim of this paper is to determine how better profiles can be built and how these affect the recommendation process based on the incorporation of temporality, i.e. the inclusion of time in the recommendation process, and topicality, i.e. the representation of texts associated with users and items using topics and their combination. To that end, we build both topically and temporally homogeneous subprofiles to represent items. The main contribution of the paper is to present two different ways of hybridising these two dimensions and to evaluate and compare them with other alternatives. Our proposals and experiments are carried out in the specific context of publication venue recommendation.
Discovering a tourism destination with social media data: BERT-based sentiment analysis
Marlon Santiago Viñán-Ludeña, Luis M. de Campos
Journal of Hospitality and Tourism Technology, 2022
Purpose The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users. Design/methodology/approach The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models. Findings The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated. Research limitations/implications The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place. Practical implications The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions. Originality/value This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.
Analyzing tourist data on Twitter: a case study in the province of Granada at Spain
Marlon Santiago Viñán-Ludeña, Luis M. de Campos
Journal of Hospitality and Tourism Insights, 2022
PurposeThe main aim of this paper is to build an approach to analyze the tourist content posted on social media. The approach incorporates information extraction, cleaning, data processing, descriptive and content analysis and can be used on different social media platforms such as Instagram, Facebook, etc. This work proposes an approach to social media analytics in traveler-generated content (TGC), and the authors use Twitter to apply this study and examine data about the city and the province of Granada.Design/methodology/approachIn order to identify what people are talking and posting on social media about places, events, restaurants, hotels, etc. the authors propose the following approach for data collection, cleaning and data analysis. The authors first identify the main keywords for the place of study. A descriptive analysis is subsequently performed, and this includes post metrics with geo-tagged analysis and user metrics, retweets and likes, comments, videos, photos and followers. The text is then cleaned. Finally, content analysis is conducted, and this includes word frequency calculation, sentiment and emotion detection and word clouds. Topic modeling was also performed with latent Dirichlet association (LDA).FindingsThe authors used the framework to collect 262,859 tweets about Granada. The most important hashtags are #Alhambra and #SierraNevada, and the most prolific user is @AlhambraCultura. The approach uses a seasonal context, and the posted tweets are divided into two periods (spring–summer and autumn–winter). Word frequency was calculated and again Granada, Alhambra are the most frequent words in both periods in English and Spanish. The topic models show the subjects that are mentioned in both languages, and although there are certain small differences in terms of language and season, the Alhambra, Sierra Nevada and gastronomy stand out as the most important topics.Research limitations/implicationsExtremely difficult to identify sarcasm, posts may be ambiguous, users may use both Spanish and English words in their tweets and tweets may contain spelling mistakes, colloquialisms or even abbreviations. Multilingualism represents also an important limitation since it is not clear how tweets written in different languages should be processed. The size of the data set is also an important factor since the greater the amount of data, the better the results. One of the largest limitations is the small number of geo-tagged tweets as geo-tagging would provide information about the place where the tweet was posted and opinions of it.Originality/valueThis study proposes an interesting way to analyze social media data, bridging tourism and social media literature in the data analysis context and contributes to discover patterns and features of the tourism destination through social media. The approach used provides the prospective traveler with an overview of the most popular places and the major posters for a particular tourist destination. From a business perspective, it informs managers of the most influential users, and the information obtained can be extremely useful for managing their tourism products in that region.
Publication Venue Recommendation Using Profiles Based on Clustering
Luis M. De Campos, Juan M. Fernandez-Luna, Juan F. Huete
IEEE Access, 2022
In this paper, we study the venue recommendation problem in order to help researchers identify a journal or conference to submit a given paper. A common approach for tackling this problem is to build profiles to define the scope of each venue. These profiles are then compared against the target paper. In our approach, we will study how clustering techniques can be used to construct topic-based profiles and an information retrieval-based approach be used to obtain the final recommendations. Additionally, we will explore how the use of authorship (which supplements the information) helps to improve the recommendations.
LDA-based term profiles for expert finding in a political setting
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Luis Redondo-Expósito
Journal of Intelligent Information Systems, 2021
Automatic construction of multi-faceted user profiles using text clustering and its application to expert recommendation and filtering problems
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Luis Redondo-Expósito
Knowledge Based Systems, 2020
Temporal and topical profiles for expert finding
Ceur Workshop Proceedings, 2020
Social media influence: a comprehensive review in general and in tourism domain
Marlon Santiago Viñán-Ludeña, Luis M. de Campos, Luis-Roberto Jacome-Galarza, Javier Sinche-Freire
Smart Innovation Systems and Technologies, 2020
Experiences and lessons learned on expert finding and document filtering in a parliamentary context
Ceur Workshop Proceedings, 2020
Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions
Luis M. de Campos, Andrés Cano, Javier G. Castellano, Serafín Moral
Statistical Applications in Genetics and Molecular Biology, 2019
Predicting IR personalization performance using pre-retrieval query predictors
Eduardo Vicente-López, Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Journal of Intelligent Information Systems, 2018
Content-based recommendation for academic expert finding
César Albusac, Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
ACM International Conference Proceeding Series, 2018
Positive unlabeled learning for building recommender systems in a parliamentary setting
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Luis Redondo-Expósito
Information Sciences, 2018
On the selection of the correct number of terms for profile construction: Theoretical and empirical analysis
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Information Sciences, 2018
Selecting relevance thresholds to improve a recommender system in a parliamentary setting
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Luis Redondo-Expósito
Ic3k 2018 Proceedings of the 10th International Joint Conference on Knowledge Discovery Knowledge Engineering and Knowledge Management, 2018
PMSC-UGR: A test collection for expert recommendation based on pubmed and scopus
César Albusac, Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018
Committee-Based Profiles for Politician Finding
Luis M. De Campos, Juan M. Fernández-Luna, Juan F. Huete
International Journal of Uncertainty Fuzziness and Knowldege Based Systems, 2017
Profile-based recommendation: A case study in a parliamentary context
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Journal of Information Science, 2017
Comparing machine learning and information retrieval-based approaches for filtering documents in a parliamentary setting
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Luis Redondo-Expósito
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
Use of textual and conceptual profiles for personalized retrieval of political documents
Eduardo Vicente-López, Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Knowledge Based Systems, 2016
Comparing monolithic and committee-based profiles for politician recommendation
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
ACM International Conference Proceeding Series, 2016
Improving automatic classifiers through interaction
Silvia Acid, Luis M. de Campos
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2016
An automatic methodology to evaluate personalized information retrieval systems
Eduardo Vicente-López, Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Antonio Tagua-Jiménez, Carmen Tur-Vigil
User Modeling and User Adapted Interaction, 2015
A lazy approach for filtering parliamentary documents
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2015
CoLe and UTAI at BioASQ 2015: Experiments with similarity based descriptor assignment
Ceur Workshop Proceedings, 2015
Concept profiles for filtering parliamentary documents
Francisco J. Ribadas, Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Ic3k 2015 Proceedings of the 7th International Joint Conference on Knowledge Discovery Knowledge Engineering and Knowledge Management, 2015
Learning parliamentary profiles for recommendation tasks
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Pável Calado, Bruno Martins
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2015
A probabilistic methodology for multilabel classification
Alfonso E. Romero, Luis M. de Campos
Intelligent Data Analysis, 2014
Approximations of causal networks by polytrees: An empirical study
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2014
Uncertainty management using probability intervals
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2014
Personalization of parliamentary document retrieval using different user profiles
Ceur Workshop Proceedings, 2014
CoLe and UTAI participation at the 2014 BioASQ semantic indexing challenge
Ceur Workshop Proceedings, 2014
Using personalization to improve XML retrieval
Luis M. de Campos, Juan M. Fernandez-Luna, Juan F. Huete, Eduardo Vicente-Lopez
IEEE Transactions on Knowledge and Data Engineering, 2014
XML search personalization strategies using query expansion, reranking and a search engine modification
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Eduardo Vicente-López
Proceedings of the ACM Symposium on Applied Computing, 2013
Score-based methods for learning Markov boundaries by searching in constrained spaces
Silvia Acid, Luis M. de Campos, Moisés Fernández
Data Mining and Knowledge Discovery, 2013
Two hierarchical text categorization approaches for BioASQ semantic indexing challenge
Ceur Workshop Proceedings, 2013
Using past-prediction accuracy in recommender systems
Juan F. Huete, J.M. Fernández-Luna, Luis M. de Campos, Miguel A. Rueda-Morales
Information Sciences, 2012
Evaluation methods and strategies for the interactive use of classifiers
Silvia Acid, Luis M. de Campos, Moisés Fernández
International Journal of Human Computer Studies, 2012
Minimum redundancy maximum relevancy versus score-based methods for learning Markov boundaries
Silvia Acid, Luis M. de Campos, Moises Fernandez
International Conference on Intelligent Systems Design and Applications Isda, 2011
Bayesian networks classifiers for gene-expression data
Luis M. de Campos, Andres Cano, Javier G. Castellano, Serafin Moral
International Conference on Intelligent Systems Design and Applications Isda, 2011
A content-based approach to relevance feedback in XML-IR for content and structure queries
Kdir 2010 Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, 2010
Managing structured queries in probabilistic XML retrieval systems
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Carlos Martín-Dancausa
Information Processing and Management, 2010
Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Miguel A. Rueda-Morales
International Journal of Approximate Reasoning, 2010
Link-based text classification using Bayesian networks
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Andrés R. Masegosa, Alfonso E. Romero
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2010
Using second-hand information in collaborative recommender systems
L. M. de Campos, J. M. Fernández-Luna, J. F. Huete, M. A. Rueda-Morales
Soft Computing, 2010
Content-oriented relevance feedback in XML-IR using the garnata information retrieval system
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Carlos Martín-Dancausa
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
Probabilistic methods for link-based classification at INEX 2008
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Alfonso E. Romero
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
New utility models for the garnata information retrieval system at INEX'08
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Carlos Martín-Dancausa, Alfonso E. Romero
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
Measuring predictive capability in collaborative filtering
Luis M. de Campos, Juan M. Fernandez-Luna, Juan F. Huete, Miguel A. Rueda-Morales
Recsys 09 Proceedings of the 3rd ACM Conference on Recommender Systems, 2009
An integrated system for managing the Andalusian Parliament's digital library
Luis M. de Campos, Juan M. Fernández‐Luna, Juan F. Huete, Carlos J. Martín‐Dancausa, Antonio Tagua‐Jiménez, Carmen Tur‐Vigil
Program, 2009
Managing uncertainty in group recommending processes
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Miguel A. Rueda-Morales
User Modeling and User Adapted Interaction, 2009
Bayesian network models for hierarchical text classification from a thesaurus
Luis M. de Campos, Alfonso E. Romero
International Journal of Approximate Reasoning, 2009
Hierarchical naive bayes models for representing user profiles
J. F. Huete, L. M. de Campos, J. M. Fernandez-Luna, M. A. Rueda-Morales
ACM SIGIR 2008 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Proceedings, 2008
An integrated system for accessing the digital library of the parliament of Andalusia: Segmentation, annotation and retrieval of transcriptions and videos
Pattern Recognition in Information Systems Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems Pris 2008 in Conjunction with Iceis 2008, 2008
A video segmentation and annotation tool for parliamentary recordings and transcriptions
Mccsis 08 Iadis Multi Conference on Computer Science and Information Systems Proceedings of Informatics 2008 and Data Mining 2008, 2008
Retrieving medical records using Bayesian networks
Luis M. De Campos
Medical Informatics Concepts Methodologies Tools and Applications, 2008
Probabilistic methods for structured document classification at INEX07
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Alfonso E. Romero
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
The Garnata information retrieval system at INEX07
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Carlos Martín-Dancausa, Alfonso E. Romero
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
Collaborative recommendations using bayesian networks and linguistic modelling
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
A collaborative recommender system based on probabilistic inference from fuzzy observations
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Fuzzy Sets and Systems, 2008
Thesaurus-Based Automatic Indexing
Luis M. de Campos
Handbook of Research on Text and Web Mining Technologies Volume I II, 2008
Using structural knowledge in a Content-Based Recommender System
L. M. de CAMPOS, J. M. FERNÁNDEZ-LUNA, J. F. HUETE, M. A. RUEDA-MORALES
World Scientific Proceedings Series on Computer Engineering and Information Science 1 Computational Intelligence in Decision and Control Proceedings of the 8th International Flins Conference, 2008
Group recommending: A methodological approach based on Bayesian networks
Luis M. de Campos, Juan M. Fernandez-Luna, Juan F. Huete, Miguel A. Rueda-Morales
Proceedings International Conference on Data Engineering, 2007
Bayesian network learning algorithms using structural restrictions
Luis M. de Campos, Javier G. Castellano
International Journal of Approximate Reasoning, 2007
Automatic indexing from a thesaurus using Bayesian networks: Application to the classification of parliamentary initiatives
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Alfonso E. Romero
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
Average and majority gates: Combining information by means of Bayesian networks
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Miguel A. Rueda-Morales
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
Influence diagrams and structured retrieval: garnata implementing the SID and CID models at INEX'06
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Alfonso E. Romero
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
A scoring function for learning Bayesian networks based on mutual information and conditional independence tests
Journal of Machine Learning Research, 2006
A theoretical framework for web categorization in hierarchical directories using Bayesian Networks
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Studies in Fuzziness and Soft Computing, 2006
E-Bay.Net: Helping users to buy in e-commerce applications
Proceedings 4th Conference of the European Society for Fuzzy Logic and Technology and 11th French Days on Fuzzy Logic and Applications Eusflat Lfa 2005 Joint Conference, 2005
Query propagation in possibilistic information retrieval networks
Proceedings 4th Conference of the European Society for Fuzzy Logic and Technology and 11th French Days on Fuzzy Logic and Applications Eusflat Lfa 2005 Joint Conference, 2005
On the use of restrictions for learning Bayesian networks
Luis M. de Campos, Javier G. Castellano
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2005
Improving the context-based influence diagram model for structured document retrieval: Removing topological restrictions and adding new evaluation methods
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Lecture Notes in Computer Science, 2005
Learning Bayesian network classifiers: Searching in a space of partially directed acyclic graphs
Silvia Acid, Luis M. de Campos, Javier G. Castellano
Machine Learning, 2005
A decision-based approach for recommending in hierarchical domains
L. M. de Campos, J. M. Fernández-Luna, M. Gómez, J. F. Huete
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2005
Clustering terms in the Bayesian network retrieval model: A new approach with two term-layers
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Applied Soft Computing Journal, 2004
A comparison of learning algorithms for Bayesian networks: A case study based on data from an emergency medical service
Silvia Acid, Luis M. de Campos, Juan M. Fernández-Luna, Susana Rodrı́guez, José Marı́a Rodrı́guez, José Luis Salcedo
Artificial Intelligence in Medicine, 2004
Using context information in structured document retrieval: An approach based on influence diagrams
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Information Processing and Management, 2004
Bayesian networks and information retrieval: An introduction to the special issue
Luis M de Campos, Juan M Fernández-Luna, Juan F Huete
Information Processing and Management, 2004
The BNR model: Foundations and performance of a Bayesian network-based retrieval model
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
International Journal of Approximate Reasoning, 2003
An iterated local search algorithm for learning Bayesian networks with restarts based on conditional independence tests
Luis M. De Campos, Juan M. Fernández-Luna, J. Miguel Puerta
International Journal of Intelligent Systems, 2003
Implementing relevance feedback in the Bayesian network retrieval model
Luis M. de Campos, Juan M. Fernández‐Luna, Juan F. Huete
Journal of the American Society for Information Science and Technology, 2003
An information retrieval model based on simple Bayesian networks
Silvia Acid, Luis M. De Campos, Juan M. Fernández-Luna, Juan F. Huete
International Journal of Intelligent Systems, 2003
Searching for Bayesian network structures in the space of restricted acyclic partially directed graphs
S. Acid, L. M. De Campos
Journal of Artificial Intelligence Research, 2003
Partial abductive inference in Bayesian networks by using probability trees
Iceis 2003 Proceedings of the 5th International Conference on Enterprise Information Systems, 2003
Improving the efficiency of the Bayesian network retrieval model by reducing relationships between terms
LUIS M. DE CAMPOS, JUAN M. FERNÁNDEZ-LUNA, JUAN F. HUETE
International Journal of Uncertainty Fuzziness and Knowldege Based Systems, 2003
Ranking structured documents using utility theory in the Bayesian Network Retrieval Model
Fabio Crestani, Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2003
A multi-layered Bayesian Network model for structured document retrieval
Fabio Crestani, Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science, 2003
Ant colony optimization for learning Bayesian networks
Luis M. de Campos, Juan M. Fernández-Luna, José A. Gámez, José M. Puerta
International Journal of Approximate Reasoning, 2002
Partial abductive inference in Bayesian belief networks - An evolutionary computation approach by using problem-specific genetic operators
L.M. de Campos, J.A. Gamez, S. Moral
IEEE Transactions on Evolutionary Computation, 2002
Local search methods for learning Bayesian networks using a modified neighborhood in the space of DAGs
Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science, 2002
A layered Bayesian network model for document retrieval
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2002
A hybrid methodology for learning belief networks: BENEDICT
Silvia Acid, Luis M. de Campos
International Journal of Approximate Reasoning, 2001
Partial abductive inference in Bayesian belief networks by simulated annealing
Luis M. de Campos, José A. Gámez, Serafı́n Moral
International Journal of Approximate Reasoning, 2001
Estimating probability values from an incomplete dataset
Silvia Acid, Luis M.de Campos, Juan F. Huete
International Journal of Approximate Reasoning, 2001
Accelerating chromosome evaluation for partial abductive inference in Bayesian networks by means of explanation set absorption
Luis M. de Campos, José A. Gámez, Serafı́n Moral
International Journal of Approximate Reasoning, 2001
Measurement of possibility distributions
LUIS M. DE CAMPOS, JUAN F. HUETE
International Journal of General Systems, 2001
Stochastic local algorithms for learning belief networks: Searching in the space of the orderings
Luis M. de Campos, J. Miguel Puerta
Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science, 2001
Simplifying explanations in Bayesian belief networks
LUIS M. de CAMPOS, JOSE A. GÁMEZ, SERAFÍN MORAL
International Journal of Uncertainty Fuzziness and Knowldege Based Systems, 2001
Relevance feedback in the bayesian network retrieval model: An approach based on term instantiation
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2001
The search of causal orderings: A short cut for learning belief networks
Silvia Acid, Luis M. de Campos, Juan F. Huete
Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science, 2001
Using bayesian networks to model emergency medical services
Silvia Acid, Luis M. de Campos, Susana Rodríguez, José María Rodríguez, José Luis Salcedo
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2001
A new approach for learning belief networks using independence criteria
Luis M. de Campos, Juan F. Huete
International Journal of Approximate Reasoning, 2000
Learning right sized belief networks by means of a hybrid methodology
Silvia Acid, Luis M. De Campos
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2000
Building Bayesian network-based information retrieval systems
L.M. de Campos, J.M. Fernandez, J.F. Huete
Proceedings International Workshop on Database and Expert Systems Applications DEXA, 2000
Independence concepts in possibility theory: Part II
Luis M. de Campos, Juan F. Huete
Fuzzy Sets and Systems, 1999
Independence concepts in possibility theory: Part I
Luis M. de Campos, Juan F. Huete
Fuzzy Sets and Systems, 1999
Partial abductive inference in Bayesian belief networks using a genetic algorithm
L.M. de Campos, J.A. Gámez, S. Moral
Pattern Recognition Letters, 1999
Independency relationships and learning algorithms for singly connected networks
LUIS M. DE CAMPOS
Journal of Experimental and Theoretical Artificial Intelligence, 1998
On the use of independence relationships for learning simplified belief networks
Luis M. de Campos, Juan F. Huete
International Journal of Intelligent Systems, 1997
Removing partial inconsistency in valuationbased systems
Luis M. de Campos, Seraf�n Moral
International Journal of Intelligent Systems, 1997
Characterizations of decomposable dependency models
L. M. De Campos
Journal of Artificial Intelligence Research, 1996
Axiomatic treatment of possibilistic independence
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 1995
Learning rules for a fuzzy inference model
Luis M. de Campos, Serafín Moral
Fuzzy Sets and Systems, 1993
Learning non probabilistic belief networks
Luis M. de Campos, Juan F. Huete
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 1993
A fuzzy inference model based on an uncertainty forward propagation approach
Luis M. de Campos, Antonio González
International Journal of Approximate Reasoning, 1993
Learning causal polytrees
Juan F. Huete, Luis M. de Campos
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 1993
Propagation of linguistic labels in causal networks
1993 IEEE International Conference on Fuzzy Systems, 1993
Characterization and comparison of sugeno and choquet integrals
Luis M. de Campos, M. Jorge
Fuzzy Sets and Systems, 1992
Propagating uncertain information forward
Luis M. de Campos, Serafin Moral
International Journal of Intelligent Systems, 1992
A unified approach to define fuzzy integrals
Luis M. de Campos, María T. Lamata, Serafín Moral
Fuzzy Sets and Systems, 1991
Updating uncertain information
Serafín Moral, Luis M. De Campos
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 1991
Learning with CASTLE
S. Acid, L. M. Campos, A. González, R. Molina, N. Pérez de la Blanca
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 1991
Distances between fuzzy measures through associated probabilities: Some applications
Luis M. de Campos, Maria T. Lamata, Serafin Moral
Fuzzy Sets and Systems, 1990
The concept of conditional fuzzy measure
Luis M. de Campos, María T. Lamata, Serafín Moral
International Journal of Intelligent Systems, 1990
Convergence properties of the monotone expectation and its application to the extension of fuzzy measures
Manuel J. Bolaños Carmona, Luis M. de Campos Ibañez, Antonio Gonzalez Muñoz
Fuzzy Sets and Systems, 1989
Representation of fuzzy measures through probabilities
Luis Miguel de Campos Ibañez, Manuel Jorge Bolaños Carmona
Fuzzy Sets and Systems, 1989
A subjective approach for ranking fuzzy numbers
Luis Miguel de Campos Ibáñez, Antonio González Muñoz
Fuzzy Sets and Systems, 1989

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