Macarena Espinilla

AN EXTENDED HIERARCHICAL LINGUISTIC MODEL FOR DECISION‐MAKING PROBLEMS

In multi‐expert decision making (MEDM) problems the experts provide their preferences about the alternatives according to their knowledge. Because they can have different knowledge, educational backgrounds, or experiences, it seems logical that they might use different evaluation scales to express their opinions. In the present article, we focus on decision problems defined in uncertain contexts where such uncertainty is modeled by means of linguistic information, therefore the decision makers would use different linguistic scales to express their evaluations on the alternatives, i.e., multigranular linguistic scales. Several computational approaches have been presented to manage multigranular linguistic scales in decision problems. Although they provide good results in some cases, still present limitations. A new approach, so‐called extended linguistic hierarchies, is presented here for managing multigranular linguistic scales to overcome those limitations, an MEDM case study is given to illustrate the proposed method.

A Knowledge Based Recommender System with Multigranular Linguistic Information

Recommender systems are applications that have emerged in the e-commerce area in order to assist users in their searches in electronic shops. These shops usually offer a wide range of items that cover the necessities of a great variety of users. Nevertheless, searching in such a wide range of items could be a very difficult and time-consuming task. Recommender systems assist users to find out suitable items by means of recommendations based on information provided by different sources such as: other users, experts, item features, etc. Most of the recommender systems force users to provide their preferences or necessities using an unique numerical scale of information fixed in advance. In spite of this information is usually related to opinions, tastes and perceptions, therefore, it seems that is usually better expressed in a qualitative way, with linguistic terms, than in a quantitative way, with precise numbers. We propose a Knowledge Based Recommender System that uses the fuzzy linguistic approach to de...

FLINTSTONES: A fuzzy linguistic decision tools enhancement suite based on the 2-tuple linguistic model and extensions

Uncertainty in real world decision making problems not always has probabilistic nature, in such cases the use of linguistic information to model and manage such an uncertainty has given good results. The adoption of linguistic information implies the accomplishment of processes of computing with words to solve linguistic decision making problems. In the specialized literature, several computational models can be found to carry out such processes. However, there is a shortage of software tools that develop and implement these computational models. The 2-tuple linguistic model has been widely used to operate with linguistic information in decision problems due to the fact that provides linguistic results that are accurate and easy to understand for human beings. Furthermore, another advantage of the 2-tuple linguistic model is the existence of different extensions to accomplish processes of computing with words in complex decision frameworks. Due to these reasons, in this paper a fuzzy linguistic decision tools enhancement suite so-called Flintstones is proposed to solve linguistic decision making problems based on the 2-tuple linguistic model and its extensions. Additionally, the Flintstones website is also presented, this website has been deployed and includes a repository of case studies and datasets for different linguistic decision making problems. Finally, a case study solved by Flintstones is illustrated in order to show its performance, usefulness and effectiveness.

A Linguistic Multigranular Sensory Evaluation Model for Olive Oil

Evaluation is a process that analyzes elements in order to achieve different objectives such as quality inspection, marketing and other fields in industrial companies. This paper focuses on sensory evaluation where the evaluated items are assessed by a panel of experts according to the knowledge acquired via human senses. In these evaluation processes the information provided by the experts implies uncertainty, vagueness and imprecision. The use of the Fuzzy Linguistic Approach32 has provided successful results modelling such a type of information. In sensory evaluation it may happen that the panel of experts have more or less degree knowledge of about the evaluated items or indicators. So, it seems suitable that each expert could express their preferences in different linguistic term sets based on their own knowledge. In this paper, we present a sensory evaluation model that manages multigranular linguistic evaluation framework based on a decision analysis scheme. This model will be applied to the sensor...

REJA: A Georeferenced Hybrid Recommender System for Restaurants

Recommender systems have become a key tool in marketing processes in e-commerce, because they provide an added value to Web–based applications in order to keep customers. In the tourist sector, the use of tourist Web based sites has got a great success due to the fact that the easy integration of tourist business processes in Web based tools. In this contribution, we introduce a hybrid recommender system for restaurants, collaborative and knowledge-based, that is able to provide recommendations in any required situation by the users/customers; besides it provides information referred by Google Maps, regarding the recommendations. Such a system has been developed for our province, Jaén (Spain), but it can be easily extended for any other geographic area.

A COMPARATIVE STUDY OF HETEROGENEOUS DECISION ANALYSIS APPROACHES APPLIED TO SUSTAINABLE ENERGY EVALUATION

The evaluation of sustainable energy policies supports the selection of the best policy to put it in practice. In this evaluation, stakeholders may express their preferences in different domains, considering their diverse background and the imprecision and uncertainty of the related information, as well as the nature of assessed criteria. Therefore, these evaluation problems require the selection of an adequate approach to manage such a heterogeneous framework. In this paper, we review three approaches with different strategies to deal with heterogeneous information and apply them to the evaluation of sustainable energy policies, with the view of analyzing their influence in a complex evaluation process, mainly in terms of interpretability and understandability.

Computational intelligence in decision making

In this preface we stress the relevance of the traditional collaboration between Engineering and any field of Mathematics in order to build intelligent decision-aid tools, as it is illustrated by the twelve papers contained in this Special Issue. These papers, selected by means of a standard peer review process after an open call, offer an interesting variety of models, approaches and techniques, to be applied within different specific problems. Each paper is introduced in this preface and is developed in the subsequent article. Moreover, in this preface we also claim for a more intense collaboration between decision engineers and other fields that study human brain behavior, like Neurology, Psychology, Sociology and Linguistics. Because if we really want to procure intelligent tools for decision aid, we should start by taking the human brain as our first reference, as it is the most efficient machinery we have found in order to deal with complex, uncertain, incomplete and even apparently inconsistent information. We hope that readers will enjoy this Special Issue devoted to Computational Intelligence in Decision Making.

A Knowledge Based Recommender System Based on Consistent Preference Relations

E-commerce companies have developed many methods and tools in order to personalize their web sites and services according to users’ necessities and tastes. The most successful and widespread are the recommender systems. The aim of these systems is to lead people to interesting items through recommendations. Sometimes, these systems face situations in which there is a lack of information and this implies unsuccessful results. In this chapter we propose a knowledge based recommender system designed to overcome these situations. The proposed system is able to compute recommendations from scarce information. Our proposal will consist in gathering user’s preference information over several examples using an incomplete preference relation. The system will complete this relation and exploit it in order to obtain a user profile that will be utilized to generate good recommendations.

Environment Simulation for the Promotion of the Open Data Initiative

The development, testing and evaluation of novel approaches to Intelligent Environment data processing require access to datasets which are of high quality, validated and annotated. Access to such datasets is limited due to issues including cost, flexibility, practicality, and a lack of a globally standardized data format. These limitations are detrimental to the progress of research. This paper provides an overview of the Open Data Initiative and the use of simulation software (IE Sim) to provide a platform for the objective assessment and comparison of activity recognition solutions. To demonstrate the approach, a dataset was generated and distributed to 3 international research organizations. Results from this study demonstrate that the approach is capable of providing a platform for benchmarking and comparison of novel approaches.

Computational Intelligence for Smart Environments

Smart environments are a multi-disciplinary research area within which technology is pervasively and ubiquitously deployed to ensure that actions are supported in an appropriate and sensitive manner. Such a solution is possible thanks to the mechanisms which support the aggregation and fusion of data along with intelligent data analytics techniques to offer appropriate decisions based on the data gleaned from the environment. In this context, the core approaches of computational intelligence (Fuzzy Logic, Neural Networks, Evolutionary Computation and Probabilistic Reasoning) are excellent tools to be employed in smarts environments. This special issue, produced following the 10th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2016), held in Canary Islands, (Spain) from November, 29th to December 2nd, 2016 is a culmination of submissions to an open call for papers with a focus on recent advancements within smart environments through the usage of computational intelligence techniques. This special issue comprises seven articles which were submitted to the call for papers and accepted following peer review. Each of the articles presents advances in research in smart environments applied to arrange of interesting and relevant application domains. We hope that this special issue provides an inspirational collection of ideas, techniques, and methodologies for smart environments through use of computational intelligence techniques which will continue to stimulate further research within this state-of-the-art domain. In the first paper, “Particle swarm optimization and harmony search based clustering and routing in wireless sensor networks” by Veena Anand and Sudhakar Pandey, an approach for improving network lifetime by using particle swarm optimization based clustering and harmony search based routing in wireless sensor networks is described. Similarly, in the context of the wireless sensor networks, Sridevi Ponmalar P. and Jawahar Senthil Kumar V. present the paper entitled “Hybrid firefly variants algorithm for localization optimization in wireless sensor network”. In this paper, bio-inspired hybrid algorithms are analyzed, designed and implemented to optimize the localization error in wireless sensor networks. The paper entitled “Classifier optimized for resource-constrained pervasive systems and energyefficiency” is proposed by Niklas Karvonen, Lara Lorna Jimenez, Miguel Gomez Simon, Joakim Nilsson, Josef Received 26 June 2017