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

Anomaly-based network intrusion detection enhancement by prediction threshold adaptation of binary classification models

Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the performance (accuracy) of anomaly-based network Intrusion Detection Systems (IDS) that are built using predictive models in a batch-learning setup. This thesis investigates how adapting the discriminating threshold of model predictions, specifically to the evaluated traffic, improves the detection rates of these Intrusion Detection models. Specifically, this thesis studied the adaptability features of three well known Machine Learning algorithms: C5.0, Random Forest, and Support Vector Machine. The ability of these algorithms to adapt their prediction thresholds was assessed and analysed under different scenarios that simulated real world settings using the prospective sampling approach. A new dataset (STA2018) was generated for this thesis and used for the analysis. This thesis has demonstrated empirically the importance of threshold adaptation in improving the accuracy of detection models when training and evaluation (test) traffic have different statistical properties. Further investigation was undertaken to analyse the effects of feature selection and data balancing processes on a model’s accuracy when evaluation traffic with different significant features were used. The effects of threshold adaptation on reducing the accuracy degradation of these models was statistically analysed. The results showed that, of the three compared algorithms, Random Forest was the most adaptable and had the highest detection rates. This thesis then extended the analysis to apply threshold adaptation on sampled traffic subsets, by using different sample sizes, sampling strategies and label error rates. This investigation showed the robustness of the Random Forest algorithm in identifying the best threshold. The Random Forest algorithm only needed a sample that was 0.05% of the original evaluation traffic to identify a discriminating threshold with an overall accuracy rate of nearly 90% of the optimal threshold."This research was supported and funded by the Government of the Sultanate of Oman represented by the Ministry of Higher Education and the Sultan Qaboos University." -- p. i

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

The 28th International Symposium on Temporal Representation and Reasoning (TIME 2021) was planned to take place in Klagenfurt, Austria, but had to move to an online conference due to the insecurities and restrictions caused by the pandemic. Since its frst edition in 1994, TIME Symposium is quite unique in the panorama of the scientifc conferences as its main goal is to bring together researchers from distinct research areas involving the management and representation of temporal data as well as the reasoning about temporal aspects of information. Moreover, TIME Symposium aims to bridge theoretical and applied research, as well as to serve as an interdisciplinary forum for exchange among researchers from the areas of artifcial intelligence, database management, logic and verifcation, and beyond