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

Improving adaptive bagging methods for evolving data streams

We propose two new improvements for bagging methods on evolving data streams. Recently, two new variants of Bagging were proposed: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. ASHT Bagging uses trees of different sizes, and ADWIN Bagging uses ADWIN as a change detector to decide when to discard underperforming ensemble members. We improve ADWIN Bagging using Hoeffding Adaptive Trees, trees that can adaptively learn from data streams that change over time. To speed up the time for adapting to change of Adaptive-Size Hoeffding Tree (ASHT) Bagging, we add an error change detector for each classifier. We test our improvements by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples

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

Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels

One important assumption underlying common classification models is the stationarity of the data. However, in real-world streaming applications, the data concept indicated by the joint distribution of feature and label is not stationary but drifting over time. Concept drift detection aims to detect such drifts and adapt the model so as to mitigate any deterioration in the model's predictive performance. Unfortunately, most existing concept drift detection methods rely on a strong and over-optimistic condition that the true labels are available immediately for all already classified instances. In this paper, a novel Hierarchical Hypothesis Testing framework with Request-and-Reverify strategy is developed to detect concept drifts by requesting labels only when necessary. Two methods, namely Hierarchical Hypothesis Testing with Classification Uncertainty (HHT-CU) and Hierarchical Hypothesis Testing with Attribute-wise "Goodness-of-fit" (HHT-AG), are proposed respectively under the novel framework. In experiments with benchmark datasets, our methods demonstrate overwhelming advantages over state-of-the-art unsupervised drift detectors. More importantly, our methods even outperform DDM (the widely used supervised drift detector) when we use significantly fewer labels.Comment: Published as a conference paper at IJCAI 201

Improving decision tree and neural network learning for evolving data-streams

High-throughput real-time Big Data stream processing requires fast incremental algorithms that keep models consistent with most recent data. In this scenario, Hoeffding Trees are considered the state-of-the-art single classifier for processing data streams and they are widely used in ensemble combinations. This thesis is devoted to the improvement of the performance of algorithms for machine learning/artificial intelligence on evolving data streams. In particular, we focus on improving the Hoeffding Tree classifier and its ensemble combinations, in order to reduce its resource consumption and its response time latency, achieving better throughput when processing evolving data streams. First, this thesis presents a study on using Neural Networks (NN) as an alternative method for processing data streams. The use of random features for improving NNs training speed is proposed and important issues are highlighted about the use of NN on a data stream setup. These issues motivated this thesis to go in the direction of improving the current state-of-the-art methods: Hoeffding Trees and their ensemble combinations. Second, this thesis proposes the Echo State Hoeffding Tree (ESHT), as an extension of the Hoeffding Tree to model time-dependencies typically present in data streams. The capabilities of the new proposed architecture on both regression and classification problems are evaluated. Third, a new methodology to improve the Adaptive Random Forest (ARF) is developed. ARF has been introduced recently, and it is considered the state-of-the-art classifier in the MOA framework (a popular framework for processing evolving data streams). This thesis proposes the Elastic Swap Random Forest, an extension to ARF that reduces the number of base learners in the ensemble down to one third on average, while providing similar accuracy than the standard ARF with 100 trees. And finally, a last contribution on a multi-threaded high performance scalable ensemble design that is highly adaptable to a variety of hardware platforms, ranging from server-class to edge computing. The proposed design achieves throughput improvements of 85x (Intel i7), 143x (Intel Xeon parsing from memory), 10x (Jetson TX1, ARM) and 23x (X-Gene2, ARM) compared to single-threaded MOA on i7. In addition, the proposal achieves 75% parallel efficiency when using 24 cores on the Intel Xeon.Procesar grandes flujos de datos (Big Data Streams, BDS) en tiempo real requiere el uso de algoritmos incrementales rápidos que mantengan los modelos consistentes con los datos más recientes. En este escenario, los Hoeffding Trees (HT) se consideran el clasificador simple más avanzado para procesar BDS, razon por la cual son ampliamente usados como base a la hora de combinar clasificadores en Ensembles. Esta tesis está dedicada a la mejora del rendimiento de algoritmos para Machine Learning/Iteligencia Artificial en BDS que evolucionan con el tiempo (es decir, BDS cuya distribución estadística cambia con el tiempo). En particular, nuestro objetivo es mejorar el Hoeffding Tree y sus combinaciones en Ensembles, con el objetivo de reducir el consumo de recursos y la latencia en el tiempo de respuesta, logrando un mejor rendimiento al procesar BDS que evolucionan en el tiempo. Primero, se presenta un estudio sobre el uso de redes neuronales (NN) con parámetros aleatorios como un método alternativo para procesar BDS con el objetivo de mejorar la velocidad de entrenamiento de Nns. También se destacan problemas importantes derivados del uso de NN para BDS. Como consecuencia, esta tesis tomo la dirección de mejorar los métodos de vanguardia en BDS: Hoeffding Trees y sus combinaciones en Ensembles. Segundo, se propone el Echo State Hoeffding Tree (ESHT), como una extensión del HT para modelar las dependencias temporales típicamente presentes en BDS. La nueva arquitectura propuesta se evalúa tanto en problemas de regresión como de clasificación. Tercero, se propone una extensión para el Adaptive Random Forest (ARF), publicado recientemente y considerado como el clasificador mas potente implementado en MOA (un framework muy popular para procesar BDS). Proponemos el Elastic Swap Random Forest para reducir el número de clasificadores en el ensemble a un tercio en promedio, al tiempo se mantiene un accuracy similar a la de un ARF estándar con 100 árboles. Finalmente, la última contribución de esta tesis es una arquitectura de Ensembles multi hilo para procesar BDS. Nuestro diseño es altamente adaptable a una variedad de plataformas de hardware, que van desde servidores hasta pequeños dispositivos en el Edge Computing (pej, Internet de las Cosas). El diseño propuesto logra mejoras de rendimiento de 85x (Intel i7), 143x (análisis de Intel Xeon desde la memoria), 10x (Jetson TX1, ARM) y 23x (X-Gene2, ARM) en comparación con MOA (un solo proceso) en un Intel i7. Además, la propuesta logra una eficiencia paralela del 75 \% cuando se usan 24 núcleos en el Intel Xeon.Postprint (published version