The focus of this thesis is on methods for supervised learning on streaming data. In spite of a significant amount of existing work on this topic, there is arguably a number of “gaps” to be filled and open questions to be addressed.
First, while model-based approaches to machine learning, such as decision tree induction, have been explored quite extensively in the context of data streams, com- paratively little attention has been paid to the paradigm of instance-based learning so far. In light of a number of potential advantages of the latter, this is indeed somewhat surprising. In particular, thanks to the “lightweight” nature of instance- based learning and its conceptual simplicity, instance-based concept representations are presumably much more flexible and much easier to adapt to changes of a dynamic environment. Therefore, we develop and implement an instance-based learner called IBLStreams that is applicable to both classification and regression problems.
In search of experimental procedures that could be used to validate our conjec- ture and to systematically compare model-based and instance-based methods with regard to their ability of adapting to concept drift, we found that existing procedures commonly used in the field are not fully appropriate for this purpose. Therefore, we propose a new type of experimental analysis, called recovery analysis, which is aimed at assessing the ability of a learner to discover a concept change quickly, and to take appropriate actions to maintain the quality and generalization performance of the model.
Fuzzy machine learning takes advantage of tools and techniques from fuzzy logic to develop methods for machine learning in general and learning on data streams in particular. As for the latter, however, the focus has almost been exclusively on the problem of regression so far. In this thesis, we therefore elaborate on the suitability on fuzzy methods for classification on data streams. More specifically, we develop an evolving version of so-called fuzzy pattern tree learning, which has recently been introduced in [82, 146] as a promising alternative to fuzzy rule models.
statistics, has not been considered in the context of data streams so far. Survival analysis is about the analysis of temporal “events” or, more specifically, questions regarding the temporal distribution of (duration between) the occurrence of events and their dependence on covariates of the data sources. To this end, we develop an incremental, adaptive version of survival analysis, namely an adaptive variant of a model that is closely related to the well-known Cox proportional hazard model.
The thesis is outlined in the following way:
Chapter 2: Background. This chapter offers an introduction to supervised learn- ing, with focus on learning from data streams; it also presents an overview of the related work and its development.
Chapter 3: Instance-Based Classification and Regression. This chapter intro- duces a nonparametric approach for classification and regression tasks on non- stationary streams. This approach is an extension of IBL-DS [17], which intro- duces three important factors that have to be considered when maintaining a case base of observed examples. These factors are the temporal relevance, the spatial relevance and the consistency aspect of a training example. Parts of this chapter were published in [152, 151].
Chapter 4: Evolving Fuzzy Pattern Trees. This chapter discusses the role of fuzzy logic in data-driven systems on data streams. Here, we present an evolving variant of the fuzzy pattern trees which we introduced in [156, 157].
Chapter 5: Survival Analysis on Event Streams. This chapter introduces a sur- vival analysis method for streams of events. As a proof of concept, we apply the proposed method on two types of streams: the stream of occurring earth- quakes and the stream of Twitter1 data. The work presented in this chapter is published in [150, 153, 155].
Chapter 6: Recovery Analysis for Adaptive Learning. This chapter discusses assessing the learning capability of an adaptive learner; it suggests a new type of performance comparison based on the learner’s ability to recover after suffering from a concept drift. Parts of this chapter were published in [154, 149].
The thesis is concluded in Chapter 7, in which we summarize the different methods developed in the thesis. As a supporting material we add the following appendixes:
1
Appendix A: Methods. This appendix gives a brief introduction to the ap- proaches used for comparison throughout the thesis.
Appendix B: MOA. This appendix introduces the MOA framework, used for performing the majority of our experiments.
Appendix C: M-Tree. Here, we introduce the index structure M-Tree.
Appendix D: Data sets. This appendix explains the utilized real, synthetic and event data streams, in the thesis. It also explains how the different types of concept change can be simulated.
Appendix E: Incremental Statistics. A set of incremental and adaptive descrip- tive statistical measures are derived in this appendix.
Chapter 2
Background
Learning from data streams extends the research area of data mining and machine learning by forming methods, parallel to the conventional learning methods, that cover the same topics in the streaming setting.
As previously explained in Section 1.2, streams of data prevail when data sources cease to generate small amounts of data that can be handled as a single batch and begin to generate continuous streams of data that are: (i) of a high throughput, (ii) with an implicit temporal order, (iii) possibility of limitless length and (iv) exhibiting a potentially changing concept, see [14].
The immense size of the data combined with the changing nature of the learned concepts lead to the emergence and development of new topics invented to tackle various learning tasks under the challenge of streamed data. These advancements lead to the enrichment of machine learning.
Learning from data streams has been considered for many learning tasks such as supervised learning [67, 1, 69, 106], non-supervised learning [49, 5, 122] and frequent itemset mining [36, 25, 36, 37]. Many supervised learning methods have been adapted for the streaming setting such as soft computing [10, 11, 111], active learning [173, 185, 161] and ensemble methods [185, 34, 137, 175].
This thesis mainly develops methods that work in the supervised setting, which makes other settings less relevant to the discussed topics here, except the work intro- duced in Chapter 5 which adopts a different type of supervision. For this reason, we present an overview in Section 2.2 of the related work, with the focus on the super- vised learning methods on data streams. The work in Chapter 5 develops a survival analysis method on data streams; this chapter presents its own statistical background in Section 5.2.
Section 2.1 introduces the definition of a machine learning task and presents the different types of learning problems. Next, the supervised learning model on data
streams is motivated in Section 2.2 and the different types of concept changes are discussed in Section 2.3. In Section 2.4, an overview of change detection methods is presented.
2.1
Machine Learning
A non-formal definition of machine learning by Thomas M. Mitchell [119] declares it as “The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.” In other words, for well-defined problems such as matrix multiplication, one can simply design an algorithm that transforms the two inputs into one output representing the result of the needed product. Different algorithms may compete in their complexity and performance, however, they still deliver the same output by definition.
In contrast to the previous problem, the task of recognizing whether an image depicts an adult male or a female, which is usually simple for humans to solve, is very hard to transform into a computer program that is able to perform this recognition. Machine learning aims at granting computers the ability to learn and find suitable algorithms to solve such problems by only seeing examples and solutions of the studied problems. The learner should extract knowledge from the presented examples, in means of the best suitable model, with the aim of producing correct solutions for similar problems.
For a more formal definition of learning, we use another quote from Thomas M. Mitchell [119]. “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P , if its performance at tasks in T , as measured by P , improves with experience E.” The direct projection of this definition on the aforementioned example maps the task T to the differentiation between male and female figures in an image. The experience E is the presented example image, annotated with the gender of the appearing person. Finally, the target is to compute predictions, for new images, that are as correct as possible, i.e. to achieve a high classification rate as a performance measure.
Learning tasks
Machine learning focuses mainly on two tasks, which vary depending on the avail- ability of a supervision:
observed examples. The input space serves the role of describing and representing the experiences to learn from. The output space contains the different values the prediction can take. A supervised learner tries to model the dependencies between samples drawn from the spaceX and their observed target values from Y, i.e. inducing a modelM: X → Y.
In the previous example, the input space X can be the space representing the
n× n pixels in a picture, Y is a dichotomy containing only two values: male and
female. This type of problem is also called classification whenever the prediction space contains categorical values; the supervised learning task is called regression when the target space is numerical.
Unsupervised learning: In this setting, the task is to find patterns and relations in
the given data without any awareness of an output, neither in the form of a category nor in the form of a numeric target value. The most practiced approach of unsuper- vised learning is clustering. The aim of clustering is to find groupings of the input data based on different criteria such as their similarity as in k -means [112], density as in DBSCAN [60], etc. The absence of an objective evaluation criterion of the found clusters makes clustering of a subjective nature, as stated by [61]: “clustering is in the eye of the beholder.”
The discussed learning scenarios, most of the time, assume the availability of the data before initializing the learning process. This assumption enjoys the benefit of having the whole data set stored on a permanent medium, and fitting it as a whole in the available memory. This assumption grants the learning process the advantage of accessing the data multiple times, probably through a number of scans. The resulting induced model(s) are then validated in order to perform the model selection. Finally, the selected model is deployed and tested.