Adaptive Online Sequential ELM for Concept Drift Tackling

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect underfitting condition.Comment: Hindawi Publishing. Computational Intelligence and Neuroscience Volume 2016 (2016), Article ID 8091267, 17 pages Received 29 January 2016, Accepted 17 May 2016. Special Issue on "Advances in Neural Networks and Hybrid-Metaheuristics: Theory, Algorithms, and Novel Engineering Applications". Academic Editor: Stefan Hauf

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Moving Learning Machine Towards Fast Real-Time Applications: A High-Speed FPGA-based Implementation of the OS-ELM Training Algorithm

Currently, there are some emerging online learning applications handling data streams in real-time. The On-line Sequential Extreme Learning Machine (OS-ELM) has been successfully used in real-time condition prediction applications because of its good generalization performance at an extreme learning speed, but the number of trainings by a second (training frequency) achieved in these continuous learning applications has to be further reduced. This paper proposes a performance-optimized implementation of the OS-ELM training algorithm when it is applied to real-time applications. In this case, the natural way of feeding the training of the neural network is one-by-one, i.e., training the neural network for each new incoming training input vector. Applying this restriction, the computational needs are drastically reduced. An FPGA-based implementation of the tailored OS-ELMalgorithm is used to analyze, in a parameterized way, the level of optimization achieved. We observed that the tailored algorithm drastically reduces the number of clock cycles consumed for the training execution up to approximately the 1%. This performance enables high-speed sequential training ratios, such as 14 KHz of sequential training frequency for a 40 hidden neurons SLFN, or 180 Hz of sequential training frequency for a 500 hidden neurons SLFN. In practice, the proposed implementation computes the training almost 100 times faster, or more, than other applications in the bibliography. Besides, clock cycles follows a quadratic complexity O(N 2), with N the number of hidden neurons, and are poorly influenced by the number of input neurons. However, it shows a pronounced sensitivity to data type precision even facing small-size problems, which force to use double floating-point precision data types to avoid finite precision arithmetic effects. In addition, it has been found that distributed memory is the limiting resource and, thus, it can be stated that current FPGA devices can support OS-ELM-based on-chip learning of up to 500 hidden neurons. Concluding, the proposed hardware implementation of the OS-ELM offers great possibilities for on-chip learning in portable systems and real-time applications where frequent and fast training is required

Incremental Learning Through Unsupervised Adaptation in Video Face Recognition

Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Resumo] Durante a última década, os métodos baseados en deep learning trouxeron un salto significativo no rendemento dos sistemas de visión artificial. Unha das claves neste éxito foi a creación de grandes conxuntos de datos perfectamente etiquetados para usar durante o adestramento. En certa forma, as redes de deep learning resumen esta enorme cantidade datos en prácticos vectores multidimensionais. Por este motivo, cando as diferenzas entre os datos de adestramento e os adquiridos durante o funcionamento dos sistemas (debido a factores como o contexto de adquisición) son especialmente notorias, as redes de deep learning son susceptibles de sufrir degradación no rendemento. Mentres que a solución inmediata a este tipo de problemas sería a de recorrer a unha recolección adicional de imaxes, co seu correspondente proceso de etiquetado, esta dista moito de ser óptima. A gran cantidade de posibles variacións que presenta o mundo visual converten rápido este enfoque nunha tarefa sen fin. Máis aínda cando existen aplicacións específicas nas que esta acción é difícil, ou incluso imposible, de realizar debido a problemas de custos ou de privacidade. Esta tese propón abordar todos estes problemas usando a perspectiva da adaptación. Así, a hipótese central consiste en asumir que é posible utilizar os datos non etiquetados adquiridos durante o funcionamento para mellorar o rendemento que obteríamos con sistemas de recoñecemento xerais. Para isto, e como proba de concepto, o campo de estudo da tese restrinxiuse ao recoñecemento de caras. Esta é unha aplicación paradigmática na cal o contexto de adquisición pode ser especialmente relevante. Este traballo comeza examinando as diferenzas intrínsecas entre algúns dos contextos específicos nos que se pode necesitar o recoñecemento de caras e como estas afectan ao rendemento. Desta maneira, comparamos distintas bases de datos (xunto cos seus contextos) entre elas, usando algúns dos descritores de características máis avanzados e así determinar a necesidade real de adaptación. A partir desta punto, pasamos a presentar o método novo, que representa a principal contribución da tese: o Dynamic Ensemble of SVM (De-SVM). Este método implementa a capacidade de adaptación utilizando unha aprendizaxe incremental non supervisada na que as súas propias predicións se usan como pseudo-etiquetas durante as actualizacións (a estratexia de auto-adestramento). Os experimentos realizáronse baixo condicións de vídeo-vixilancia, un exemplo paradigmático dun contexto moi específico no que os procesos de etiquetado son particularmente complicados. As ideas claves de De-SVM probáronse en diferentes sub-problemas de recoñecemento de caras: a verificación de caras e recoñecemento de caras en conxunto pechado e en conxunto aberto. Os resultados acadados mostran un comportamento prometedor en termos de adquisición de coñecemento sen supervisión así como robustez contra impostores. Ademais, este rendemento é capaz de superar a outros métodos do estado da arte que non posúen esta capacidade de adaptación.[Resumen] Durante la última década, los métodos basados en deep learning trajeron un salto significativo en el rendimiento de los sistemas de visión artificial. Una de las claves en este éxito fue la creación de grandes conjuntos de datos perfectamente etiquetados para usar durante el entrenamiento. En cierta forma, las redes de deep learning resumen esta enorme cantidad datos en prácticos vectores multidimensionales. Por este motivo, cuando las diferencias entre los datos de entrenamiento y los adquiridos durante el funcionamiento de los sistemas (debido a factores como el contexto de adquisición) son especialmente notorias, las redes de deep learning son susceptibles de sufrir degradación en el rendimiento. Mientras que la solución a este tipo de problemas es recurrir a una recolección adicional de imágenes, con su correspondiente proceso de etiquetado, esta dista mucho de ser óptima. La gran cantidad de posibles variaciones que presenta el mundo visual convierten rápido este enfoque en una tarea sin fin. Más aún cuando existen aplicaciones específicas en las que esta acción es difícil, o incluso imposible, de realizar; debido a problemas de costes o de privacidad. Esta tesis propone abordar todos estos problemas usando la perspectiva de la adaptación. Así, la hipótesis central consiste en asumir que es posible utilizar los datos no etiquetados adquiridos durante el funcionamiento para mejorar el rendimiento que se obtendría con sistemas de reconocimiento generales. Para esto, y como prueba de concepto, el campo de estudio de la tesis se restringió al reconocimiento de caras. Esta es una aplicación paradigmática en la cual el contexto de adquisición puede ser especialmente relevante. Este trabajo comienza examinando las diferencias entre algunos de los contextos específicos en los que se puede necesitar el reconocimiento de caras y así como sus efectos en términos de rendimiento. De esta manera, comparamos distintas ba ses de datos (y sus contextos) entre ellas, usando algunos de los descriptores de características más avanzados para así determinar la necesidad real de adaptación. A partir de este punto, pasamos a presentar el nuevo método, que representa la principal contribución de la tesis: el Dynamic Ensemble of SVM (De- SVM). Este método implementa la capacidad de adaptación utilizando un aprendizaje incremental no supervisado en la que sus propias predicciones se usan cómo pseudo-etiquetas durante las actualizaciones (la estrategia de auto-entrenamiento). Los experimentos se realizaron bajo condiciones de vídeo-vigilancia, un ejemplo paradigmático de contexto muy específico en el que los procesos de etiquetado son particularmente complicados. Las ideas claves de De- SVM se probaron en varios sub-problemas del reconocimiento de caras: la verificación de caras y reconocimiento de caras de conjunto cerrado y conjunto abierto. Los resultados muestran un comportamiento prometedor en términos de adquisición de conocimiento así como de robustez contra impostores. Además, este rendimiento es capaz de superar a otros métodos del estado del arte que no poseen esta capacidad de adaptación.[Abstract] In the last decade, deep learning has brought an unprecedented leap forward for computer vision general classification problems. One of the keys to this success is the availability of extensive and wealthy annotated datasets to use as training samples. In some sense, a deep learning network summarises this enormous amount of data into handy vector representations. For this reason, when the differences between training datasets and the data acquired during operation (due to factors such as the acquisition context) are highly marked, end-to-end deep learning methods are susceptible to suffer performance degradation. While the immediate solution to mitigate these problems is to resort to an additional data collection and its correspondent annotation procedure, this solution is far from optimal. The immeasurable possible variations of the visual world can convert the collection and annotation of data into an endless task. Even more when there are specific applications in which this additional action is difficult or simply not possible to perform due to, among other reasons, cost-related problems or privacy issues. This Thesis proposes to tackle all these problems from the adaptation point of view. Thus, the central hypothesis assumes that it is possible to use operational data with almost no supervision to improve the performance we would achieve with general-purpose recognition systems. To do so, and as a proof-of-concept, the field of study of this Thesis is restricted to face recognition, a paradigmatic application in which the context of acquisition can be especially relevant. This work begins by examining the intrinsic differences between some of the face recognition contexts and how they directly affect performance. To do it, we compare different datasets, and their contexts, against each other using some of the most advanced feature representations available to determine the actual need for adaptation. From this point, we move to present the novel method, representing the central contribution of the Thesis: the Dynamic Ensembles of SVM (De-SVM). This method implements the adaptation capabilities by performing unsupervised incremental learning using its own predictions as pseudo-labels for the update decision (the self-training strategy). Experiments are performed under video surveillance conditions, a paradigmatic example of a very specific context in which labelling processes are particularly complicated. The core ideas of De-SVM are tested in different face recognition sub-problems: face verification and, the more complex, general closed- and open-set face recognition. In terms of the achieved results, experiments have shown a promising behaviour in terms of both unsupervised knowledge acquisition and robustness against impostors, surpassing the performances achieved by state-of-the-art non-adaptive methods.Funding and Technical Resources For the successful development of this Thesis, it was necessary to rely on series of indispensable means included in the following list: • Working material, human and financial support primarily by the CITIC and the Computer Architecture Group of the University of A Coruña and CiTIUS of University of Santiago de Compostela, along with a PhD grant funded by Xunta the Galicia and the European Social Fund. • Access to bibliographical material through the library of the University of A Coruña. • Additional funding through the following research projects: State funding by the Ministry of Economy and Competitiveness of Spain (project TIN2017-90135-R MINECO, FEDER)

Towards a Self-Sufficient Face Verification System

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] The absence of a previous collaborative manual enrolment represents a significant handicap towards designing a face verification system for face re-identification purposes. In this scenario, the system must learn the target identity incrementally, using data from the video stream during the operational authentication phase. So, manual labelling cannot be assumed apart from the first few frames. On the other hand, even the most advanced methods trained on large-scale and unconstrained datasets suffer performance degradation when no adaptation to specific contexts is performed. This work proposes an adaptive face verification system, for the continuous re-identification of target identity, within the framework of incremental unsupervised learning. Our Dynamic Ensemble of SVM is capable of incorporating non-labelled information to improve the performance of any model, even when its initial performance is modest. The proposal uses the self-training approach and is compared against other classification techniques within this same approach. Results show promising behaviour in terms of both knowledge acquisition and impostor robustness.This work has received financial support from the Spanish government (project TIN2017-90135-R MINECO (FEDER)), from The Consellaría de Cultura, Educación e Ordenación Universitaria (accreditations 2016–2019, EDG431G/01 and ED431G/08), and reference competitive groups (2017–2020, and ED431C 2017/04), and from the European Regional Development Fund (ERDF). Eric López-López has received financial support from the Xunta de Galicia and the European Union (European Social Fund – ESF)Xunta de Galicia; EDG431G/01Xunta de Galicia; ED431G/08Xunta de Galicia; ED431C 2017/0

Ensemble based on randomised neural networks for online data stream regression in presence of concept drift

The big data paradigm has posed new challenges for the Machine Learning algorithms, such as analysing continuous flows of data, in the form of data streams, and dealing with the evolving nature of the data, which cause a phenomenon often referred to in the literature as concept drift. Concept drift is caused by inconsistencies between the optimal hypotheses in two subsequent chunks of data, whereby the concept underlying a given process evolves over time, which can happen due to several factors including change in consumer preference, economic dynamics, or environmental conditions. This thesis explores the problem of data stream regression with the presence of concept drift. This problem requires computationally efficient algorithms that are able to adapt to the various types of drift that may affect the data. The development of effective algorithms for data streams with concept drift requires several steps that are discussed in this research. The first one is related to the datasets required to assess the algorithms. In general, it is not possible to determine the occurrence of concept drift on real-world datasets; therefore, synthetic datasets where the various types of concept drift can be simulated are required. The second issue is related to the choice of the algorithm. The ensemble algorithms show many advantages to deal with concept drifting data streams, which include flexibility, computational efficiency and high accuracy. For the design of an effective ensemble, this research analyses the use of randomised Neural Networks as base models, along with their optimisation. The optimisation of the randomised Neural Networks involves design and tuning hyperparameters which may substantially affect its performance. The optimisation of the base models is an important aspect to build highly accurate and computationally efficient ensembles. To cope with the concept drift, the existing methods either require setting fixed updating points, which may result in unnecessary computations or slow reaction to concept drift, or rely on drifting detection mechanism, which may be ineffective due to the difficulty to detect drift in real applications. Therefore, the research contributions of this thesis include the development of a new approach for synthetic dataset generation, development of a new hyperparameter optimisation algorithm that reduces the search effort and the need of prior assumptions compared to existing methods, the analysis of the effects of randomised Neural Networks hyperparameters, and the development of a new ensemble algorithm based on bagging meta-model that reduces the computational effort over existing methods and uses an innovative updating mechanism to cope with concept drift. The algorithms have been tested on synthetic datasets and validated on four real-world datasets from various application domains

Anomaly-Based Intrusion Detection by Modeling Probability Distributions of Flow Characteristics

In recent years, with the increased use of network communication, the risk of compromising the information has grown immensely. Intrusions have evolved and become more sophisticated. Hence, classical detection systems show poor performance in detecting novel attacks. Although much research has been devoted to improving the performance of intrusion detection systems, few methods can achieve consistently efficient results with the constant changes in network communications. This thesis proposes an intrusion detection system based on modeling distributions of network flow statistics in order to achieve a high detection rate for known and stealthy attacks. The proposed model aggregates the traffic at the IP subnetwork level using a hierarchical heavy hitters algorithm. This aggregated traffic is used to build the distribution of network statistics for the most frequent IPv4 addresses encountered as destination. The obtained probability density functions are learned by the Extreme Learning Machine method which is a single-hidden layer feedforward neural network. In this thesis, different sequential and batch learning strategies are proposed in order to analyze the efficiency of this proposed approach. The performance of the model is evaluated on the ISCX-IDS 2012 dataset consisting of injection attacks, HTTP flooding, DDoS and brute force intrusions. The experimental results of the thesis indicate that the presented method achieves an average detection rate of 91% while having a low misclassification rate of 9%, which is on par with the state-of-the-art approaches using this dataset. In addition, the proposed method can be utilized as a network behavior analysis tool specifically for DDoS mitigation, since it can isolate aggregated IPv4 addresses from the rest of the network traffic, thus supporting filtering out DDoS attacks

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

An ensemble based on neural networks with random weights for online data stream regression

Most information sources in the current technological world are generating data sequentially and rapidly, in the form of data streams. The evolving nature of processes may often cause changes in data distribution, also known as concept drift, which is difficult to detect and causes loss of accuracy in supervised learning algorithms. As a consequence, online machine learning algorithms that are able to update actively according to possible changes in the data distribution are required. Although many strategies have been developed to tackle this problem, most of them are designed for classification problems. Therefore, in the domain of regression problems, there is a need for the development of accurate algorithms with dynamic updating mechanisms that can operate in a computational time compatible with today’s demanding market. In this article, the authors propose a new bagging ensemble approach based on Neural Network with Random Weights for online data stream regression. The proposed method improves the data prediction accuracy as well as minimises the required computational time compared to a recent algorithm for online data stream regression from literature. The experiments are carried out using four synthetic datasets to evaluate the algorithm's response to concept drift, along with four benchmark datasets from different industries. The results indicate improvement in data prediction accuracy, effectiveness in handling concept drift and much faster updating times compared to the existing available approach. Additionally, the use of Design of Experiments as an effective tool for hyperparameter tuning is demonstrated

New Archive-Based Ant Colony Optimization Algorithms for Learning Predictive Rules from Data

Data mining is the process of extracting knowledge and patterns from data. Classification and Regression are among the major data mining tasks, where the goal is to predict a value of an attribute of interest for each data instance, given the values of a set of predictive attributes. Most classification and regression problems involve continuous, ordinal and categorical attributes. Currently Ant Colony Optimization (ACO) algorithms have focused on directly handling categorical attributes only; continuous attributes are transformed using a discretisation procedure in either a preprocessing stage or dynamically during the rule creation. The use of a discretisation procedure has several limitations: (i) it increases the computational runtime, since several candidates values need to evaluated; (ii) requires access to the entire attribute domain, which in some applications all data is not available; (iii) the values used to create discrete intervals are not optimised in combination with the values of other attributes. This thesis investigates the use of solution archive pheromone model, based on Ant Colony Optimization for mixed-variable (ACOMV) algorithm, to directly cope with all attribute types. Firstly, an archive-based ACO classification algorithm is presented, followed by an automatic design framework to generate new configuration of ACO algorithms. Then, we addressed the challenging problem of mining data streams, presenting a new ACO algorithm in combination with a hybrid pheromone model. Finally, the archive-based approach is extended to cope with regression problems. All algorithms presented are compared against well-known algorithms from the literature using publicly available data sets. Our results have been shown to improve the computational time while maintaining a competitive predictive performance