Mitigating Concept Drift via Rejection

Göpfert JP, Hammer B, Wersing H. Mitigating Concept Drift via Rejection. In: Kurkova V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I, eds. Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Lecture Notes in Computer Science. Vol 11139. Cham: Springer; 2018

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

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-level, algorithm-level, and ensemble approaches. However, there is a lack of standardized and agreed-upon procedures on how to evaluate these algorithms. This work presents a taxonomy of algorithms for imbalanced data streams and proposes a standardized, exhaustive, and informative experimental testbed to evaluate algorithms in a collection of diverse and challenging imbalanced data stream scenarios. The experimental study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced data streams that combine static and dynamic class imbalance ratios, instance-level difficulties, concept drift, real-world and semi-synthetic datasets in binary and multi-class scenarios. This leads to the largest experimental study conducted so far in the data stream mining domain. We discuss the advantages and disadvantages of state-of-the-art classifiers in each of these scenarios and we provide general recommendations to end-users for selecting the best algorithms for imbalanced data streams. Additionally, we formulate open challenges and future directions for this domain. Our experimental testbed is fully reproducible and easy to extend with new methods. This way we propose the first standardized approach to conducting experiments in imbalanced data streams that can be used by other researchers to create trustworthy and fair evaluation of newly proposed methods. Our experimental framework can be downloaded from https://github.com/canoalberto/imbalanced-streams

AdaDeepStream: streaming adaptation to concept evolution in deep neural networks

Typically, Deep Neural Networks (DNNs) are not responsive to changing data. Novel classes will be incorrectly labelled as a class on which the network was previously trained to recognise. Ideally, a DNN would be able to detect changing data and adapt rapidly with minimal true-labelled samples and without catastrophically forgetting previous classes. In the Online Class Incremental (OCI) field, research focuses on remembering all previously known classes. However, real-world systems are dynamic, and it is not essential to recall all classes forever. The Concept Evolution field studies the emergence of novel classes within a data stream. This paper aims to bring together these fields by analysing OCI Convolutional Neural Network (CNN) adaptation systems in a concept evolution setting by applying novel classes in patterns. Our system, termed AdaDeepStream, offers a dynamic concept evolution detection and CNN adaptation system using minimal true-labelled samples. We apply activations from within the CNN to fast streaming machine learning techniques. We compare two activation reduction techniques. We conduct a comprehensive experimental study and compare our novel adaptation method with four other state-of-the-art CNN adaptation methods. Our entire system is also compared to two other novel class detection and CNN adaptation methods. The results of the experiments are analysed based on accuracy, speed of inference and speed of adaptation. On accuracy, AdaDeepStream outperforms the next best adaptation method by 27% and the next best combined novel class detection/CNN adaptation method by 24%. On speed, AdaDeepStream is among the fastest to process instances and adapt

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

Bio-mimetic Spiking Neural Networks for unsupervised clustering of spatio-temporal data

Spiking neural networks aspire to mimic the brain more closely than traditional artificial neural networks. They are characterised by a spike-like activation function inspired by the shape of an action potential in biological neurons. Spiking networks remain a niche area of research, perform worse than the traditional artificial networks, and their real-world applications are limited. We hypothesised that neuroscience-inspired spiking neural networks with spike-timing-dependent plasticity demonstrate useful learning capabilities. Our objective was to identify features which play a vital role in information processing in the brain but are not commonly used in artificial networks, implement them in spiking networks without copying constraints that apply to living organisms, and to characterise their effect on data processing. The networks we created are not brain models; our approach can be labelled as artificial life. We performed a literature review and selected features such as local weight updates, neuronal sub-types, modularity, homeostasis and structural plasticity. We used the review as a guide for developing the consecutive iterations of the network, and eventually a whole evolutionary developmental system. We analysed the model’s performance on clustering of spatio-temporal data. Our results show that combining evolution and unsupervised learning leads to a faster convergence on the optimal solutions, better stability of fit solutions than each approach separately. The choice of fitness definition affects the network’s performance on fitness-related and unrelated tasks. We found that neuron type-specific weight homeostasis can be used to stabilise the networks, thus enabling longer training. We also demonstrated that networks with a rudimentary architecture can evolve developmental rules which improve their fitness. This interdisciplinary work provides contributions to three fields: it proposes novel artificial intelligence approaches, tests the possible role of the selected biological phenomena in information processing in the brain, and explores the evolution of learning in an artificial life system