Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams

The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers with diverse learning styles and different drift detectors. Intuitively, the current 'best' (classifier, detector) pair is application dependent and may change as a result of the stream evolution. Our research builds on this observation. We introduce the \mbox{Tornado} framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. In our framework, all (classifier, detector) pairs proceed, in parallel, to construct models against the evolving data streams. At any point in time, we select the pair which currently yields the best performance. We further incorporate two novel stacking-based drift detection methods, namely the \mbox{FHDDMS} and \mbox{FHDDMS}_{add} approaches. The experimental evaluation confirms that the current 'best' (classifier, detector) pair is not only heavily dependent on the characteristics of the stream, but also that this selection evolves as the stream flows. Further, our \mbox{FHDDMS} variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art.Comment: 42 pages, and 14 figure

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System

Concept Drift Adaptation with Incremental–Decremental SVM

Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is currently an open problem. We introduce the Adaptive Incremental–Decremental SVM (AIDSVM), a model that adjusts the shifting window width using the Hoeffding statistical test. We evaluate AIDSVM performance on both synthetic and real-world drift datasets. Experiments show a significant accuracy improvement when encountering concept drift, compared with similar drift detection models defined in the literature. The AIDSVM is efficient, since it is not retrained from scratch after the shifting window slides

New perspectives and methods for stream learning in the presence of concept drift.

153 p.Applications that generate data in the form of fast streams from non-stationary environments, that is,those where the underlying phenomena change over time, are becoming increasingly prevalent. In thiskind of environments the probability density function of the data-generating process may change overtime, producing a drift. This causes that predictive models trained over these stream data become obsoleteand do not adapt suitably to the new distribution. Specially in online learning scenarios, there is apressing need for new algorithms that adapt to this change as fast as possible, while maintaining goodperformance scores. Examples of these applications include making inferences or predictions based onfinancial data, energy demand and climate data analysis, web usage or sensor network monitoring, andmalware/spam detection, among many others.Online learning and concept drift are two of the most hot topics in the recent literature due to theirrelevance for the so-called Big Data paradigm, where nowadays we can find an increasing number ofapplications based on training data continuously available, named as data streams. Thus, learning in nonstationaryenvironments requires adaptive or evolving approaches that can monitor and track theunderlying changes, and adapt a model to accommodate those changes accordingly. In this effort, Iprovide in this thesis a comprehensive state-of-the-art approaches as well as I identify the most relevantopen challenges in the literature, while focusing on addressing three of them by providing innovativeperspectives and methods.This thesis provides with a complete overview of several related fields, and tackles several openchallenges that have been identified in the very recent state of the art. Concretely, it presents aninnovative way to generate artificial diversity in ensembles, a set of necessary adaptations andimprovements for spiking neural networks in order to be used in online learning scenarios, and finally, adrift detector based on this former algorithm. All of these approaches together constitute an innovativework aimed at presenting new perspectives and methods for the field

New perspectives and methods for stream learning in the presence of concept drift.

153 p.Applications that generate data in the form of fast streams from non-stationary environments, that is,those where the underlying phenomena change over time, are becoming increasingly prevalent. In thiskind of environments the probability density function of the data-generating process may change overtime, producing a drift. This causes that predictive models trained over these stream data become obsoleteand do not adapt suitably to the new distribution. Specially in online learning scenarios, there is apressing need for new algorithms that adapt to this change as fast as possible, while maintaining goodperformance scores. Examples of these applications include making inferences or predictions based onfinancial data, energy demand and climate data analysis, web usage or sensor network monitoring, andmalware/spam detection, among many others.Online learning and concept drift are two of the most hot topics in the recent literature due to theirrelevance for the so-called Big Data paradigm, where nowadays we can find an increasing number ofapplications based on training data continuously available, named as data streams. Thus, learning in nonstationaryenvironments requires adaptive or evolving approaches that can monitor and track theunderlying changes, and adapt a model to accommodate those changes accordingly. In this effort, Iprovide in this thesis a comprehensive state-of-the-art approaches as well as I identify the most relevantopen challenges in the literature, while focusing on addressing three of them by providing innovativeperspectives and methods.This thesis provides with a complete overview of several related fields, and tackles several openchallenges that have been identified in the very recent state of the art. Concretely, it presents aninnovative way to generate artificial diversity in ensembles, a set of necessary adaptations andimprovements for spiking neural networks in order to be used in online learning scenarios, and finally, adrift detector based on this former algorithm. All of these approaches together constitute an innovativework aimed at presenting new perspectives and methods for the field

Towards Reliable Machine Learning in Evolving Data Streams

Data streams are ubiquitous in many areas of modern life. For example, applications in healthcare, education, finance, or advertising often deal with large-scale and evolving data streams. Compared to stationary applications, data streams pose considerable additional challenges for automated decision making and machine learning. Indeed, online machine learning methods must cope with limited memory capacities, real-time requirements, and drifts in the data generating process. At the same time, online learning methods should provide a high predictive quality, stability in the presence of input noise, and good interpretability in order to be reliably used in practice. In this thesis, we address some of the most important aspects of machine learning in evolving data streams. Specifically, we identify four open issues related to online feature selection, concept drift detection, online classification, local explainability, and the evaluation of online learning methods. In these contexts, we present new theoretical and empirical findings as well as novel frameworks and implementations. In particular, we propose new approaches for online feature selection and concept drift detection that can account for model uncertainties and thus achieve more stable results. Moreover, we introduce a new incremental decision tree that retains valuable interpretability properties and a new change detection framework that allows for more efficient explanations based on local feature attributions. In fact, this is one of the first works to address intrinsic model interpretability and local explainability in the presence of incremental updates and concept drift. Along with this thesis, we provide extensive open resources related to online machine learning. Notably, we introduce a new Python framework that enables simplified and standardized evaluations and can thus serve as a basis for more comparable online learning experiments in the future. In total, this thesis is based on six publications, five of which were peer-reviewed at the time of publication of this thesis. Our work touches all major areas of predictive modeling in data streams and proposes novel solutions for efficient, stable, interpretable and thus reliable online machine learning.Datenströme sind in vielen Bereichen des modernen Lebens allgegenwärtig. Beispielsweise haben Anwendungen im Gesundheitswesen, im Bildungswesen, im Finanzwesen oder in der Werbung häufig mit großen und sich verändernden Datenströmen zu tun. Im Vergleich zu stationären Anwendungen stellen Datenströme eine erhebliche zusätzliche Herausforderung für die automatisierte Entscheidungsfindung und das maschinelle Lernen dar. So müssen Online Machine Learning-Verfahren mit begrenzten Speicherkapazitäten, Echtzeitanforderungen und Veränderungen des Daten-generierenden Prozesses zurechtkommen. Gleichzeitig sollten Online Learning-Verfahren eine hohe Vorhersagequalität, Stabilität bei Eingangsrauschen und eine gute Interpretierbarkeit aufweisen, um in der Praxis zuverlässig eingesetzt werden zu können. In dieser Arbeit befassen wir uns mit einigen der wichtigsten Aspekte des maschinellen Lernens in sich entwickelnden Datenströmen. Insbesondere identifizieren wir vier offene Fragen im Zusammenhang mit Online Feature Selection, Concept Drift Detection, Online-Klassifikation, lokaler Erklärbarkeit und der Bewertung von Online Learning-Methoden. In diesem Kontext präsentieren wir neue theoretische und empirische Erkenntnisse sowie neue Frameworks und Implementierungen. Insbesondere schlagen wir neue Ansätze für Online Feature Selection und Concept Drift Detection vor, die Unsicherheiten im Modell berücksichtigen und dadurch stabilere Ergebnisse erzielen können. Darüber hinaus stellen wir einen neuen inkrementellen Entscheidungsbaum vor, der wertvolle Eigenschaften hinsichtlich der Interpretierbarkeit einhält, sowie ein neues Framework zur Erkennung von Veränderungen, das effizientere Erklärungen auf der Grundlage lokaler Feature Attributions ermöglicht. Tatsächlich ist dies eine der ersten Arbeiten, die sich mit intrinsischer Interpretierbarkeit von Modellen und lokaler Erklärbarkeit bei inkrementellen Aktualisierungen und Concept Drift befasst. Gemeinsam mit dieser Arbeit stellen wir umfangreiche Ressourcen für Online Machine Learning zur Verfügung. Insbesondere stellen wir ein neues Python-Framework vor, das vereinfachte und standardisierte Auswertungen ermöglicht und künftig somit als Grundlage für vergleichbare Online Learning-Experimente dienen kann. Insgesamt stützt sich diese Arbeit auf sechs Publikationen, von denen fünf zum Zeitpunkt der Veröffentlichung der Dissertation bereits im Peer-Review Format begutachtet wurden. Unsere Arbeit berührt alle wichtigen Bereiche der prädiktiven Modellierung in Datenströmen und schlägt neuartige Lösungen für effizientes, stabiles, interpretierbares und damit zuverlässiges Online Machine Learning vor

Data science applications to connected vehicles: Key barriers to overcome

The connected vehicles will generate huge amount of pervasive and real time data, at very high frequencies. This poses new challenges for Data science. How to analyse these data and how to address short-term and long-term storage are some of the key barriers to overcome.JRC.C.6-Economics of Climate Change, Energy and Transpor

Continual learning from stationary and non-stationary data

Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals. Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty can be found in combining these two abilities within a single learning algorithm, since, in such scenarios, we have to balance remembering and forgetting instead of focusing only on one aspect. The presented dissertation addressed these problems in an exploratory way. Its main goal was to grasp the continual learning paradigm as a whole, analyze its different branches and tackle identified issues covering various aspects of learning from sequentially incoming data. By doing so, this work not only filled several gaps in the current continual learning research but also emphasized the complexity and diversity of challenges existing in this domain. Comprehensive experiments conducted for all of the presented contributions have demonstrated their effectiveness and substantiated the validity of the stated claims