Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

A hierarchical, fuzzy inference approach to data filtration and feature prioritization in the connected manufacturing enterprise

In manufacturing, the technology to capture and store large volumes of data developed earlier and faster than corresponding capabilities to analyze, interpret, and apply it. The result for many manufacturers is a collection of unanalyzed data and uncertainty with respect to where to begin. This paper examines big data as both an enabler and a challenge for the connected manufacturing enterprise and presents a framework that sequentially tests and selects independent variables for training applied machine learning models. Unsuitable features are discarded, and each remaining feature receives a crisp numeric output and a linguistic label, both of which are measures of the feature’s suitability. The framework is tested using three datasets employing time series, binary, and continuous input data. Results of filtered models are compared to results obtained by base, unfiltered sets of features using a proposed metric of performance-size ratio. Framework results outperform base feature sets in all tested cases, and the proposed future research will be to implement it in a case study in the electronic assembly manufacture

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Meta Heuristics based Machine Learning and Neural Mass Modelling Allied to Brain Machine Interface

New understanding of the brain function and increasing availability of low-cost-non-invasive electroencephalograms (EEGs) recording devices have made brain-computer-interface (BCI) as an alternative option to augmentation of human capabilities by providing a new non-muscular channel for sending commands, which could be used to activate electronic or mechanical devices based on modulation of thoughts. In this project, our emphasis will be on how to develop such a BCI using fuzzy rule-based systems (FRBSs), metaheuristics and Neural Mass Models (NMMs). In particular, we treat the BCI system as an integrated problem consisting of mathematical modelling, machine learning and classification. Four main steps are involved in designing a BCI system: 1) data acquisition, 2) feature extraction, 3) classification and 4) transferring the classification outcome into control commands for extended peripheral capability. Our focus has been placed on the first three steps. This research project aims to investigate and develop a novel BCI framework encompassing classification based on machine learning, optimisation and neural mass modelling. The primary aim in this project is to bridge the gap of these three different areas in a bid to design a more reliable and accurate communication path between the brain and external world. To achieve this goal, the following objectives have been investigated: 1) Steady-State Visual Evoked Potential (SSVEP) EEG data are collected from human subjects and pre-processed; 2) Feature extraction procedure is implemented to detect and quantify the characteristics of brain activities which indicates the intention of the subject.; 3) a classification mechanism called an Immune Inspired Multi-Objective Fuzzy Modelling Classification algorithm (IMOFM-C), is adapted as a binary classification approach for classifying binary EEG data. Then, the DDAG-Distance aggregation approach is proposed to aggregate the outcomes of IMOFM-C based binary classifiers for multi-class classification; 4) building on IMOFM-C, a preference-based ensemble classification framework known as IMOFM-CP is proposed to enhance the convergence performance and diversity of each individual component classifier, leading to an improved overall classification accuracy of multi-class EEG data; and 5) finally a robust parameterising approach which combines a single-objective GA and a clustering algorithm with a set of newly devised objective and penalty functions is proposed to obtain robust sets of synaptic connectivity parameters of a thalamic neural mass model (NMM). The parametrisation approach aims to cope with nonlinearity nature normally involved in describing multifarious features of brain signals

Obtención de reglas de clasificación difusas utilizando técnicas de optimización : Caso de estudio Riesgo Crediticio

El aporte central de esta tesis es la definición de un nuevo método capaz de generar un conjunto de reglas de clasificación difusas de fácil interpretación, baja cardinalidad y una buena precisión. Estas características ayudan a identificar y comprender las relaciones presentes en los datos facilitando de esta forma la toma de decisiones. El nuevo método propuesto se denomina FRvarPSO (Fuzzy Rules variable Particle Swarm Oprmization) y combina una red neuronal competitiva con una técnica de optimización basada en cúmulo de partículas de población variable para la obtención de reglas de clasificación difusas, capaces de operar sobre atributos nominales y numéricos. Los antecedentes de las reglas están formados por atributos nominales y/o condiciones difusas. La conformación de estas últimas requiere conocer el grado de pertenencia a los conjuntos difusos que definen a cada variable lingüística. Esta tesis propone tres alternativas distintas para resolver este punto. Uno de los aportes de esta tesis radica en la definición de la función de aptitud o fitness de cada partícula basada en un ”Criterio de Votación” que pondera de manera difusa la participación de las condiciones difusas en la conformación del antecedente. Su valor se obtiene a partir de los grados de pertenencia de los ejemplos que cumplen con la regla y se utiliza para reforzar el movimiento de la partícula en la dirección donde se encuentra el valor más alto. Con la utilización de PSO las partículas compiten entre ellas para encontrar a la mejor regla de la clase seleccionada. La medición se realizó sobre doce bases de datos del repositorio UCI (Machine Learning Repository) y tres casos reales en el área de crédito del Sistema Financiero del Ecuador asociadas al riesgo crediticio considerando un conjunto de variables micro y macroeconómicas. Otro de los aportes de esta tesis fue haber realizado una consideración especial en la morosidad del cliente teniendo en cuenta los días de vencimiento de la cartera otorgada; esto fue posible debido a que se tenía información del cliente en un horizonte de tiempo, una vez que el crédito se había concedido Se verificó que con este análisis las reglas difusas obtenidas a través de FRvarPSO permiten que el oficial de crédito de respuesta al cliente en menor tiempo, y principalmente disminuya el riesgo que representa el otorgamiento de crédito para las instituciones financieras. Lo anterior fue posible, debido a que al aplicar una regla difusa se toma el menor grado de pertenencia promedio de las condiciones difusas que forman el antecedente de la regla, con lo que se tiene una métrica proporcional al riesgo de su aplicación.Tesis en cotutela con la Universitat Rovira i Virgili (URV) (España).Facultad de InformáticaUniversitat Rovira i Virgil

Exploring the Application of Hybrid Evolutionary Computation Techniques to Physical Activity Recognition

This paper has been presented at: GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion.This paper focuses on the problem of physical activity recognition, i.e., the development of a system which is able to learn patterns from data in order to be able to detect which physical activity (e.g. running, walking, ascending stairs, etc.) a certain user is performing.While this field is broadly explored in the literature, there are few works that face the problem with evolutionary computation techniques. In this case, we propose a hybrid system which combines particle swarm optimization for clustering features and genetic programming combined with evolutionary strategies for evolving a population of classifiers, shaped in the form of decision trees. This system would run the segmentation, feature extraction and classification stages of the activity recognition chain.For this paper, we have used the PAMAP2 dataset with a basic preprocessing. This dataset is publicly available at UCI ML repository. Then, we have evaluated the proposed system using three different modes: a user-independent, a user-specific and a combined one. The results in terms of classification accuracy were poor for the first and the last mode, but it performed significantly well for the user-specific case. This paper aims to describe work in progress, to share early results an discuss them. There are many things that could be improved in this proposed system, but overall results were interesting especially because no manual data transformation took place.This project was partially funded by European Union's CIP Programme (ICT-PSP-2012) under grant agreement no. 325146 (SEACW project), and is supported the Spanish Ministry of Education, Culture and Sport through FPU fellowship with identifier FPU13/03917

A Semantic Model for Enhancing Data-Driven Open Banking Services

In current Open Banking services, the European Payment Services Directive (PSD2) allows the secure collection of bank customer information, on their behalf and with their consent, to analyze their financial status and needs. The PSD2 directive has lead to a massive number of daily transactions between Fintech entities which require the automatic management of the data involved, generally coming from multiple and heterogeneous sources and formats. In this context, one of the main challenges lies in defining and implementing common data integration schemes to easily merge them into knowledge-base repositories, hence allowing data reconciliation and sophisticated analysis. In this sense, Semantic Web technologies constitute a suitable framework for the semantic integration of data that makes linking with external sources possible and enhances systematic querying. With this motivation, an ontology approach is proposed in this work to operate as a semantic data mediator in real-world open banking operations. According to semantic reconciliation mechanisms, the underpinning knowledge graph is populated with data involved in PSD2 open banking transactions, which are aligned with information from invoices. A series of semantic rules is defined in this work to show how the financial solvency classification of client entities and transaction concept suggestions can be inferred from the proposed semantic model.This research has been partially funded by the Spanish Ministry of Science and Innovation via the Aether Project with grant number PID2020-112540RB-C41 (AEI/FEDER, UE), the Ministry of Industry, Commerce and Tourism via the Helix initiative with grant number AEI-010500-2020-34, and the Andalusian PAIDI program with grant number P18-RT-2799. Partial funding for open access charge: Universidad de Málag