Otimização multi-objetivo em aprendizado de máquina

Orientador: Fernando José Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Regressão logística multinomial regularizada, classificação multi-rótulo e aprendizado multi-tarefa são exemplos de problemas de aprendizado de máquina em que objetivos conflitantes, como funções de perda e penalidades que promovem regularização, devem ser simultaneamente minimizadas. Portanto, a perspectiva simplista de procurar o modelo de aprendizado com o melhor desempenho deve ser substituída pela proposição e subsequente exploração de múltiplos modelos de aprendizado eficientes, cada um caracterizado por um compromisso (trade-off) distinto entre os objetivos conflitantes. Comitês de máquinas e preferências a posteriori do tomador de decisão podem ser implementadas visando explorar adequadamente este conjunto diverso de modelos de aprendizado eficientes, em busca de melhoria de desempenho. A estrutura conceitual multi-objetivo para aprendizado de máquina é suportada por três etapas: (1) Modelagem multi-objetivo de cada problema de aprendizado, destacando explicitamente os objetivos conflitantes envolvidos; (2) Dada a formulação multi-objetivo do problema de aprendizado, por exemplo, considerando funções de perda e termos de penalização como objetivos conflitantes, soluções eficientes e bem distribuídas ao longo da fronteira de Pareto são obtidas por um solver determinístico e exato denominado NISE (do inglês Non-Inferior Set Estimation); (3) Esses modelos de aprendizado eficientes são então submetidos a um processo de seleção de modelos que opera com preferências a posteriori, ou a filtragem e agregação para a síntese de ensembles. Como o NISE é restrito a problemas de dois objetivos, uma extensão do NISE capaz de lidar com mais de dois objetivos, denominada MONISE (do inglês Many-Objective NISE), também é proposta aqui, sendo uma contribuição adicional que expande a aplicabilidade da estrutura conceitual proposta. Para atestar adequadamente o mérito da nossa abordagem multi-objetivo, foram realizadas investigações mais específicas, restritas à aprendizagem de modelos lineares regularizados: (1) Qual é o mérito relativo da seleção a posteriori de um único modelo de aprendizado, entre os produzidos pela nossa proposta, quando comparado com outras abordagens de modelo único na literatura? (2) O nível de diversidade dos modelos de aprendizado produzidos pela nossa proposta é superior àquele alcançado por abordagens alternativas dedicadas à geração de múltiplos modelos de aprendizado? (3) E quanto à qualidade de predição da filtragem e agregação dos modelos de aprendizado produzidos pela nossa proposta quando aplicados a: (i) classificação multi-classe, (ii) classificação desbalanceada, (iii) classificação multi-rótulo, (iv) aprendizado multi-tarefa, (v) aprendizado com multiplos conjuntos de atributos? A natureza determinística de NISE e MONISE, sua capacidade de lidar adequadamente com a forma da fronteira de Pareto em cada problema de aprendizado, e a garantia de sempre obter modelos de aprendizado eficientes são aqui pleiteados como responsáveis pelos resultados promissores alcançados em todas essas três frentes de investigação específicasAbstract: Regularized multinomial logistic regression, multi-label classification, and multi-task learning are examples of machine learning problems in which conflicting objectives, such as losses and regularization penalties, should be simultaneously minimized. Therefore, the narrow perspective of looking for the learning model with the best performance should be replaced by the proposition and further exploration of multiple efficient learning models, each one characterized by a distinct trade-off among the conflicting objectives. Committee machines and a posteriori preferences of the decision-maker may be implemented to properly explore this diverse set of efficient learning models toward performance improvement. The whole multi-objective framework for machine learning is supported by three stages: (1) The multi-objective modelling of each learning problem, explicitly highlighting the conflicting objectives involved; (2) Given the multi-objective formulation of the learning problem, for instance, considering loss functions and penalty terms as conflicting objective functions, efficient solutions well-distributed along the Pareto front are obtained by a deterministic and exact solver named NISE (Non-Inferior Set Estimation); (3) Those efficient learning models are then subject to a posteriori model selection, or to ensemble filtering and aggregation. Given that NISE is restricted to two objective functions, an extension for many objectives, named MONISE (Many Objective NISE), is also proposed here, being an additional contribution and expanding the applicability of the proposed framework. To properly access the merit of our multi-objective approach, more specific investigations were conducted, restricted to regularized linear learning models: (1) What is the relative merit of the a posteriori selection of a single learning model, among the ones produced by our proposal, when compared with other single-model approaches in the literature? (2) Is the diversity level of the learning models produced by our proposal higher than the diversity level achieved by alternative approaches devoted to generating multiple learning models? (3) What about the prediction quality of ensemble filtering and aggregation of the learning models produced by our proposal on: (i) multi-class classification, (ii) unbalanced classification, (iii) multi-label classification, (iv) multi-task learning, (v) multi-view learning? The deterministic nature of NISE and MONISE, their ability to properly deal with the shape of the Pareto front in each learning problem, and the guarantee of always obtaining efficient learning models are advocated here as being responsible for the promising results achieved in all those three specific investigationsDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétrica2014/13533-0FAPES

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

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

Heterogeneous recognition of bioacoustic signals for human-machine interfaces

Human-machine interfaces (HMI) provide a communication pathway between man and machine. Not only do they augment existing pathways, they can substitute or even bypass these pathways where functional motor loss prevents the use of standard interfaces. This is especially important for individuals who rely on assistive technology in their everyday life. By utilising bioacoustic activity, it can lead to an assistive HMI concept which is unobtrusive, minimally disruptive and cosmetically appealing to the user. However, due to the complexity of the signals it remains relatively underexplored in the HMI field. This thesis investigates extracting and decoding volition from bioacoustic activity with the aim of generating real-time commands. The developed framework is a systemisation of various processing blocks enabling the mapping of continuous signals into M discrete classes. Class independent extraction efficiently detects and segments the continuous signals while class-specific extraction exemplifies each pattern set using a novel template creation process stable to permutations of the data set. These templates are utilised by a generalised single channel discrimination model, whereby each signal is template aligned prior to classification. The real-time decoding subsystem uses a multichannel heterogeneous ensemble architecture which fuses the output from a diverse set of these individual discrimination models. This enhances the classification performance by elevating both the sensitivity and specificity, with the increased specificity due to a natural rejection capacity based on a non-parametric majority vote. Such a strategy is useful when analysing signals which have diverse characteristics, false positives are prevalent and have strong consequences, and when there is limited training data available. The framework has been developed with generality in mind with wide applicability to a broad spectrum of biosignals. The processing system has been demonstrated on real-time decoding of tongue-movement ear pressure signals using both single and dual channel setups. This has included in-depth evaluation of these methods in both offline and online scenarios. During online evaluation, a stimulus based test methodology was devised, while representative interference was used to contaminate the decoding process in a relevant and real fashion. The results of this research provide a strong case for the utility of such techniques in real world applications of human-machine communication using impulsive bioacoustic signals and biosignals in general

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC

Symmetric and Asymmetric Data in Solution Models

This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described