Learning from Data Streams with Randomized Forests

Non-stationary streaming data poses a familiar challenge in machine learning: the need to obtain fast and accurate predictions. A data stream is a continuously generated sequence of data, with data typically arriving rapidly. They are often characterised by a non-stationary generative process, with concept drift occurring as the process changes. Such processes are commonly seen in the real world, such as in advertising, shopping trends, environmental conditions, electricity monitoring and traffic monitoring. Typical stationary algorithms are ill-suited for use with concept drifting data, thus necessitating more targeted methods. Tree-based methods are a popular approach to this problem, traditionally focussing on the use of the Hoeffding bound in order to guarantee performance relative to a stationary scenario. However, there are limited single learners available for regression scenarios, and those that do exist often struggle to choose between similarly discriminative splits, leading to longer training times and worse performance. This limited pool of single learners in turn hampers the performance of ensemble approaches in which they act as base learners. In this thesis we seek to remedy this gap in the literature, developing methods which focus on increasing randomization to both improve predictive performance and reduce the training times of tree-based ensemble methods. In particular, we have chosen to investigate the use of randomization as it is known to be able to improve generalization error in ensembles, and is also expected to lead to fast training times, thus being a natural method of handling the problems typically experienced by single learners. We begin in a regression scenario, introducing the Adaptive Trees for Streaming with Extreme Randomization (ATSER) algorithm; a partially randomized approach based on the concept of Extremely Randomized (extra) trees. The ATSER algorithm incrementally trains trees, using the Hoeffding bound to select the best of a random selection of splits. Simultaneously, the trees also detect and adapt to changes in the data stream. Unlike many traditional streaming algorithms ATSER trees can easily be extended to include nominal features. We find that compared to other contemporary methods ensembles of ATSER trees lead to improved predictive performance whilst also reducing run times. We then demonstrate the Adaptive Categorisation Trees for Streaming with Extreme Randomization (ACTSER) algorithm, an adaption of the ATSER algorithm to the more traditional categorization scenario, again showing improved predictive performance and reduced runtimes. The inclusion of nominal features is particularly novel in this setting since typical categorization approaches struggle to handle them. Finally we examine a completely randomized scenario, where an ensemble of trees is generated prior to having access to the data stream, while also considering multivariate splits in addition to the traditional axis-aligned approach. We find that through the combination of a forgetting mechanism in linear models and dynamic weighting for ensemble members, we are able to avoid explicitly testing for concept drift. This leads to fast ensembles with strong predictive performance, whilst also requiring fewer parameters than other contemporary methods. For each of the proposed methods in this thesis, we demonstrate empirically that they are effective over a variety of different non-stationary data streams, including on multiple types of concept drift. Furthermore, in comparison to other contemporary data streaming algorithms, we find the biggest improvements in performance are on noisy data streams.Engineers Gat

Improving Hoeffding Trees

Modern information technology allows information to be collected at a far greater rate than ever before. So fast, in fact, that the main problem is making sense of it all. Machine learning offers promise of a solution, but the field mainly focusses on achieving high accuracy when data supply is limited. While this has created sophisticated classification algorithms, many do not cope with increasing data set sizes. When the data set sizes get to a point where they could be considered to represent a continuous supply, or data stream, then incremental classification algorithms are required. In this setting, the effectiveness of an algorithm cannot simply be assessed by accuracy alone. Consideration needs to be given to the memory available to the algorithm and the speed at which data is processed in terms of both the time taken to predict the class of a new data sample and the time taken to include this sample in an incrementally updated classification model. The Hoeffding tree algorithm is a state-of-the-art method for inducing decision trees from data streams. The aim of this thesis is to improve this algorithm. To measure improvement, a comprehensive framework for evaluating the performance of data stream algorithms is developed. Within the framework memory size is fixed in order to simulate realistic application scenarios. In order to simulate continuous operation, classes of synthetic data are generated providing an evaluation on a large scale. Improvements to many aspects of the Hoeffding tree algorithm are demonstrated. First, a number of methods for handling continuous numeric features are compared. Second, tree prediction strategy is investigated to evaluate the utility of various methods. Finally, the possibility of improving accuracy using ensemble methods is explored. The experimental results provide meaningful comparisons of accuracy and processing speeds between different modifications of the Hoeffding tree algorithm under various memory limits. The study on numeric attributes demonstrates that sacrificing accuracy for space at the local level often results in improved global accuracy. The prediction strategy shown to perform best adaptively chooses between standard majority class and Naive Bayes prediction in the leaves. The ensemble method investigation shows that combining trees can be worthwhile, but only when sufficient memory is available, and improvement is less likely than in traditional machine learning. In particular, issues are encountered when applying the popular boosting method to streams

Hoeffding Tree Algorithms for Anomaly Detection in Streaming Datasets: A Survey

This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets. The objective is to provide the effectiveness of using Hoeffding Trees as a machine learning algorithm solution for the problem of detecting anomalies in streaming cyber datasets. In this survey we categorize the existing research works of Hoeffding Trees which can be feasible for this type of study into the following: surveying distributed Hoeffding Trees, surveying ensembles of Hoeffding Trees and surveying existing techniques using Hoeffding Trees for anomaly detection. These categories are referred to as compositions within this paper and were selected based on their relation to streaming data and the flexibility of their techniques for use within different domains of streaming data. We discuss the relevance of how combining the techniques of the proposed research works within these compositions can be used to address the anomaly detection problem in streaming cyber datasets. The goal is to show how a combination of techniques from different compositions can solve a prominent problem, anomaly detection

A Survey on Concept Drift Adaptation

Concept drift primarily refers to an online supervised learning scenario when the relation between the in- put data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for re- searchers, industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art

Adaptive random forests for evolving data stream classification

Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources

Memory Models for Incremental Learning Architectures

Losing V. Memory Models for Incremental Learning Architectures. Bielefeld: Universität Bielefeld; 2019.Technological advancement leads constantly to an exponential growth of generated data in basically every domain, drastically increasing the burden of data storage and maintenance. Most of the data is instantaneously extracted and available in form of endless streams that contain the most current information. Machine learning methods constitute one fundamental way of processing such data in an automatic way, as they generate models that capture the processes behind the data. They are omnipresent in our everyday life as their applications include personalized advertising, recommendations, fraud detection, surveillance, credit ratings, high-speed trading and smart-home devices. Thereby, batch learning, denoting the offline construction of a static model based on large datasets, is the predominant scheme. However, it is increasingly unfit to deal with the accumulating masses of data in given time and in particularly its static nature cannot handle changing patterns. In contrast, incremental learning constitutes one attractive alternative that is a very natural fit for the current demands. Its dynamic adaptation allows continuous processing of data streams, without the necessity to store all data from the past, and results in always up-to-date models, even able to perform in non-stationary environments. In this thesis, we will tackle crucial research questions in the domain of incremental learning by contributing new algorithms or significantly extending existing ones. Thereby, we consider stationary and non-stationary environments and present multiple real-world applications that showcase merits of the methods as well as their versatility. The main contributions are the following: One novel approach that addresses the question of how to extend a model for prototype-based algorithms based on cost minimization. We propose local split-time prediction for incremental decision trees to mitigate the trade-off between adaptation speed versus model complexity and run time. An extensive survey of the strengths and weaknesses of state-of-the-art methods that provides guidance for choosing a suitable algorithm for a given task. One new approach to extract valuable information about the type of change in a dataset. We contribute a biologically inspired architecture, able to handle different types of drift using dedicated memories that are kept consistent. Application of the novel methods within three diverse real-world tasks, highlighting their robustness and versatility. Investigation of personalized online models in the context of two real-world applications