Fitting ST-OWA operators to empirical data

The OWA operators gained interest among researchers as they provide a continuum of aggregation operators able to cover the whole range of compensation between the minimum and the maximum. In some circumstances, it is useful to consider a wider range of values, extending below the minimum and over the maximum. ST-OWA are able to surpass the boundaries of variation of ordinary compensatory operators. Their application requires identification of the weighting vector, the t-norm, and the t-conorm. This task can be accomplished by considering both the desired analytical properties and empirical data.<br /

Fuzzy Sets in Business Management, Finance, and Economics

This book collects fifteen papers published in s Special Issue of Mathematics titled “Fuzzy Sets in Business Management, Finance, and Economics”, which was published in 2021. These paper cover a wide range of different tools from Fuzzy Set Theory and applications in many areas of Business Management and other connected fields. Specifically, this book contains applications of such instruments as, among others, Fuzzy Set Qualitative Comparative Analysis, Neuro-Fuzzy Methods, the Forgotten Effects Algorithm, Expertons Theory, Fuzzy Markov Chains, Fuzzy Arithmetic, Decision Making with OWA Operators and Pythagorean Aggregation Operators, Fuzzy Pattern Recognition, and Intuitionistic Fuzzy Sets. The papers in this book tackle a wide variety of problems in areas such as strategic management, sustainable decisions by firms and public organisms, tourism management, accounting and auditing, macroeconomic modelling, the evaluation of public organizations and universities, and actuarial modelling. We hope that this book will be useful not only for business managers, public decision-makers, and researchers in the specific fields of business management, finance, and economics but also in the broader areas of soft mathematics in social sciences. Practitioners will find methods and ideas that could be fruitful in current management issues. Scholars will find novel developments that may inspire further applications in the social sciences

Fuzzy C-ordered medoids clustering of interval-valued data

Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data. The Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering methods based on a partitioning around medoids approach. However, one of the greatest disadvantages of this method is its sensitivity to the presence of outliers in data. This paper introduces a new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID). The Huber's M-estimators and the Yager's Ordered Weighted Averaging (OWA) operators are used in the method proposed to make it robust to outliers. The described algorithm is compared with the fuzzy c-medoids method in the experiments performed on synthetic data with different types of outliers. A real application of the FcOMdC-ID is also provided

Machine Learning Methods for Fuzzy Pattern Tree Induction

This thesis elaborates on a novel approach to fuzzy machine learning, that is, the combination of machine learning methods with mathematical tools for modeling and information processing based on fuzzy logic. More specifically, the thesis is devoted to so-called fuzzy pattern trees, a model class that has recently been introduced for representing dependencies between input and output variables in supervised learning tasks, such as classification and regression. Due to its hierarchical, modular structure and the use of different types of (nonlinear) aggregation operators, a fuzzy pattern tree has the ability to represent such dependencies in a very exible and compact way, thereby offering a reasonable balance between accuracy and model transparency. The focus of the thesis is on novel algorithms for pattern tree induction, i.e., for learning fuzzy pattern trees from observed data. In total, three new algorithms are introduced and compared to an existing method for the data-driven construction of pattern trees. While the first two algorithms are mainly geared toward an improvement of predictive accuracy, the last one focuses on eficiency aspects and seeks to make the learning process faster. The description and discussion of each algorithm is complemented with theoretical analyses and empirical studies in order to show the effectiveness of the proposed solutions

Estrategias para agregación de información guiada por cuantificadores: aplicación a las ciudades inteligentes

La agregación de información permite fusionar varios datos de entrada en un dato resumido, y es un proceso cada vez más necesario debido a la enorme cantidad de datos digitales que se generan en diferentes campos, entre ellos el de las ciudades inteligentes. La agregación guiada por cuantificadores lingüísticos es una manera de obtener métodos para la agregación de información. En este TFG, se plantea la realización de un estudio comparativo de diferentes estrategias para construir dichos métodos, basadas en el uso de varios tipos de cuantificadores. El estudio considerará datos reales pertenecientes al campo de las ciudades inteligentes.Se desarrollará una aplicación software que permita realizar la agregación de información mediante diferentes métodos, estimando el error cometido. Para evaluarla, se considerarán diferentes conjuntos de datos en el ámbito de las ciudades inteligentes. Se usará Java como lenguaje de programación, así como bibliotecas software para importar y exportar hojas de cálculo en el formato de Microsoft Excel. Para seleccionar los datos de prueba, se realizará una búsqueda exhaustiva de los datos públicamente disponibles para varias ciudades inteligentes.<br /

Trust networks for recommender systems

Recommender systems use information about their user’s profiles and relationships to suggest items that might be of interest to them. Recommenders that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional systems, provided they succeed in utilizing the additional (dis)trust information to their advantage. Such trust-enhanced recommenders consist of two main components: recommendation technologies and trust metrics (techniques which aim to estimate the trust between two unknown users.) We introduce a new bilattice-based model that considers trust and distrust as two different but dependent components, and study the accompanying trust metrics. Two of their key building blocks are trust propagation and aggregation. If user a wants to form an opinion about an unknown user x, a can contact one of his acquaintances, who can contact another one, etc., until a user is reached who is connected with x (propagation). Since a will often contact several persons, one also needs a mechanism to combine the trust scores that result from several propagation paths (aggregation). We introduce new fuzzy logic propagation operators and focus on the potential of OWA strategies and the effect of knowledge defects. Our experiments demonstrate that propagators that actively incorporate distrust are more accurate than standard approaches, and that new aggregators result in better predictions than purely bilattice-based operators. In the second part of the dissertation, we focus on the application of trust networks in recommender systems. After the introduction of a new detection measure for controversial items, we show that trust-based approaches are more effective than baselines. We also propose a new algorithm that achieves an immediate high coverage while the accuracy remains adequate. Furthermore, we also provide the first experimental study on the potential of distrust in a memory-based collaborative filtering recommendation process. Finally, we also study the user cold start problem; we propose to identify key figures in the network, and to suggest them as possible connection points for newcomers. Our experiments show that it is much more beneficial for a new user to connect to an identified key figure instead of making random connections

Modeling choice and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning

International audienceCurrent learning theory provides a comprehensive description of how humans and other animals learn, and places behavioral flexibility and automaticity at heart of adaptive behaviors. However, the computations supporting the interactions between goal-directed and habitual decision-making systems are still poorly understood. Previous functional magnetic resonance imaging (fMRI) results suggest that the brain hosts complementary computations that may differentially support goal-directed and habitual processes in the form of a dynamical interplay rather than a serial recruitment of strategies. To better elucidate the computations underlying flexible behavior, we develop a dual-system computational model that can predict both performance (i.e., participants' choices) and modulations in reaction times during learning of a stimulus–response association task. The habitual system is modeled with a simple Q-Learning algorithm (QL). For the goal-directed system, we propose a new Bayesian Working Memory (BWM) model that searches for information in the history of previous trials in order to minimize Shannon entropy. We propose a model for QL and BWM coordination such that the expensive memory manipulation is under control of, among others, the level of convergence of the habitual learning. We test the ability of QL or BWM alone to explain human behavior, and compare them with the performance of model combinations, to highlight the need for such combinations to explain behavior. Two of the tested combination models are derived from the literature, and the latter being our new proposal. In conclusion, all subjects were better explained by model combinations, and the majority of them are explained by our new coordination proposal

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies

A multi-criteria method for making tradeoffs and hard decisions spatially explicit in marine conservation planning

Identifying new marine protected areas (MPAs) typically requires considering competing priorities from a large range of stakeholders. While balancing socioeconomic losses with biodiversity gains is challenging, it is central to the planning process and will influence the effectiveness of the MPAs to be created. This paper presents a new decision-support method named Spatial Tier Framework-Ordered Weighted Averaging (STF-OWA) that allows stakeholders to share their values and explore alternative planning scenarios, by varying levels of losses and gains, in a collaborative setting. Unlike methods that aim at finding one optimal solution (e.g. Marxan), the STF-OWA provides stakeholders with alternative planning options based on weights reflecting their priorities among and between biodiversity interests (e.g. corals vs. birds) and socioeconomic interests (e.g. fishing employment vs. fishing dollars). The approach was tested in the Newfoundland and Labrador shelf bioregion, Atlantic Canada (~1.2x106 km²), using scientific survey data on groundfish, seabirds, and habitat-forming invertebrates, commercial fishing logbooks, data on marine transportation, and oil and gas activities. Results show that the STF-OWA can identify easy-to-implement conservation sites (i.e. high biodiversity with low socioeconomic activities), although they represent only 5% of the study area as an MPA often involves hard decision areas (i.e. sites with both high socioeconomic impacts and high biodiversity gains). On making tradeoffs and hard decisions spatially explicit, the STF-OWA: (1) offers various options such as cheap, cost-effective, and expensive scenarios, making the toughest conservation decisions spatially explicit -- namely, tough decisions for and against biodiversity protection and tough decisions for and against socioeconomic protection; (2) allows visualizing multiple competing interests in a solution set that provides empirical evidence that a win-win option is rare; and (3) permits delineating regions of interest (ROIs) and percent area targets within a conservation scenario that makes balancing loss and gain more spatially explicit at a finer scale. With these features available in the STF-OWA decision-support method, it is possible to identify not only the areas that minimize potential conflicts, but also areas of high importance for biological protection, and to do so without masking the tough political decisions needed in advancing conservation goals