Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

Management of data quality when integrating data with known provenance

Abstract unavailable please refer to PD

Statistical learning in complex and temporal data: distances, two-sample testing, clustering, classification and Big Data

Programa Oficial de Doutoramento en Estatística e Investigación Operativa. 555V01[Resumo] Esta tesis trata sobre aprendizaxe estatístico en obxetos complexos, con énfase en series temporais. O problema abórdase introducindo coñecemento sobre o dominio do fenómeno subxacente, mediante distancias e características. Proponse un contraste de dúas mostras basado en distancias e estúdase o seu funcionamento nun gran abanico de escenarios. As distancias para clasificación e clustering de series temporais acadan un incremento da potencia estatística cando se aplican a contrastes de dúas mostras. O noso test compárase de xeito favorable con outros métodos gracias á súa flexibilidade ante diferentes alternativas. Defínese unha nova distancia entre series temporais mediante un xeito innovador de comparar as distribucións retardadas das series. Esta distancia herda o bo funcionamento empírico doutros métodos pero elimina algunhas das súas limitacións. Proponse un método de predicción baseada en características das series. O método combina diferentes algoritmos estándar de predicción mediante unha suma ponderada. Os pesos desta suma veñen dun modelo que se axusta a un conxunto de entrenamento de gran tamaño. Propónse un método de clasificación distribuida, baseado en comparar, mediante unha distancia, as funcións de distribución empíricas do conxuto de proba común e as dos datos que recibe cada nodo de cómputo.[Resumen] Esta tesis trata sobre aprendizaje estadístico en objetos complejos, con énfasis en series temporales. El problema se aborda introduciendo conocimiento del dominio del fenómeno subyacente, mediante distancias y características. Se propone un test de dos muestras basado en distancias y se estudia su funcionamiento en un gran abanico de escenarios. La distancias para clasificación y clustering de series temporales consiguen un incremento de la potencia estadística cuando se aplican al tests de dos muestras. Nuestro test se compara favorablemente con otros métodos gracias a su flexibilidad antes diferentes alternativas. Se define una nueva distancia entre series temporales mediante una manera innovadora de comparar las distribuciones retardadas de la series. Esta distancia hereda el buen funcionamiento empírico de otros métodos pero elimina algunas de sus limitaciones. Se propone un método de predicción basado en características de las series. El método combina diferentes algoritmos estándar de predicción mediante una suma ponderada. Los pesos de esta suma salen de un modelo que se ajusta a un conjunto de entrenamiento de gran tamaño. Se propone un método de clasificación distribuida, basado en comparar, mediante una distancia, las funciones de distribución empírica del conjuto de prueba común y las de los datos que recibe cada nodo de cómputo.[Abstract] This thesis deals with the problem of statistical learning in complex objects, with emphasis on time series data. The problem is approached by facilitating the introduction of domain knoweldge of the underlying phenomena by means of distances and features. A distance-based two sample test is proposed, and its performance is studied under a wide range of scenarios. Distances for time series classification and clustering are also shown to increase statistical power when applied to two-sample testing. Our test compares favorably to other methods regarding its flexibility against different alternatives. A new distance for time series is defined by considering an innovative way of comparing lagged distributions of the series. This distance inherits the good empirical performance of existing methods while removing some of their limitations. A forecast method based on times series features is proposed. The method works by combining individual standard forecasting algorithms using a weighted average. These weights come from a learning model fitted on a large training set. A distributed classification algorithm is proposed, based on comparing, using a distance, the empirical distribution functions between the dataset that each computing node receives and the test set

Current Topics on Risk Analysis: ICRA6 and RISK2015 Conference

Peer ReviewedPostprint (published version

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Current Topics on Risk Analysis: ICRA6 and RISK2015 Conference

Artículos presentados en la International Conference on Risk Analysis ICRA 6/RISK 2015, celebrada en Barcelona del 26 al 29 de mayo de 2015.Peer ReviewedPostprint (published version

Journal of Telecommunications and Information Technology, 2009, nr 3

kwartalni

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases