Automated Machine Learning for Multi-Label Classification

Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand. For supervised learning tasks, most notably binary and multinomial classification, aka single-label classification (SLC), such AutoML approaches have shown promising results. However, the task of multi-label classification (MLC), where data points are associated with a set of class labels instead of a single class label, has received much less attention so far. In the context of multi-label classification, the data-specific selection and configuration of multi-label classifiers are challenging even for experts in the field, as it is a high-dimensional optimization problem with multi-level hierarchical dependencies. While for SLC, the space of machine learning pipelines is already huge, the size of the MLC search space outnumbers the one of SLC by several orders. In the first part of this thesis, we devise a novel AutoML approach for single-label classification tasks optimizing pipelines of machine learning algorithms, consisting of two algorithms at most. This approach is then extended first to optimize pipelines of unlimited length and eventually configure the complex hierarchical structures of multi-label classification methods. Furthermore, we investigate how well AutoML approaches that form the state of the art for single-label classification tasks scale with the increased problem complexity of AutoML for multi-label classification. In the second part, we explore how methods for SLC and MLC could be configured more flexibly to achieve better generalization performance and how to increase the efficiency of execution-based AutoML systems

Patterns of Nationalist Discourse in the Early Reception of the Icelandic Sagas in Britain

The unprecedented production of English translations of the Icelandic sagas in the 1860s occurred alongside widespread cultural discussion concerning ethnic-nationalism and the developing science of comparative philology. Although the relationship between these phenomena has been examined, there has been no scholarly consensus on the reality, extent, or direction of any influence between them. This thesis reports on the seminal texts which gave context to and informed the late-nineteenth-century translations of Old Norse Íslendingasögur into English, their cultural stimuli and progeny. Firstly, the thesis examines the influence of and contextual philosophies behind J. A. Blackwell’s revised edition of Northern Antiquities, and in particular its depiction of Old Norse literature as key to understanding British ancestry. The thesis then considers the impact of Blackwell’s inclusion of Walter Scott’s Eyrbyggja saga ‘Abstract’, and the extent to which this partial translation characterised subsequent attitudes to nationality. Finally, the thesis examines the wide nationalist implications of the European interest in Friðþjófs saga, and the nature of the scholarship of George Stephens, its first English translator. The results of this study demonstrate that far from following a simplistic model of cause and effect, one needs to view the development of the reception of Old Norse literature as being intricately bound with contemporary political and national interests. Previous studies have often emphasised the unconventionality of the pioneering translators; this study underlines both their reliance on wider academic discussion and the wide-spread acceptability of their ideas within Georgian and early-Victorian Britain. The study complements previous research in providing a detailed assessment of ethnic-nationalist discourse within British Old Norse scholarship and eschewing the common view that the discussion was merely a product of foreign philosophy

Lithosphere 2021 : Eleventh symposium on structure, composition and evolution of the lithosphere

Programme and extended abstract

Pattern mining for label ranking

Preferences have always been present in many tasks in our daily lives. Buying the right car, choosing a suitable house or even deciding on the food to eat, are trivial examples of decisions that reveal information, explicitly or implicitly, about our preferences. The recent trend of collecting increasing amounts of data is also true for preference data. Extracting and modeling preferences can provide us with invaluable information about the choices of groups or individuals. In areas like e-commerce, which typically deal with decisions from thousands of users, the acquisition of preferences can be a difficult task. For these reasons, artificial intelligence (in particular, machine learning) methods have been increasingly important to the discovery and automatic learning of models about preferences. In this Ph.D. project, several approaches were analyzed and proposed to deal with the LR problem. Most of which has focused on pattern mining methods.Algorithms and the Foundations of Software technolog

Pattern Mining for Label Ranking

Preferences have always been present in many tasks in our daily lives. Buying the right car, choosing a suitable house or even deciding on the food to eat, are trivial examples of decisions that reveal information, explicitly or implicitly, about our preferences. The recent trend of collecting increasing amounts of data is also true for preference data. Extracting and modeling preferences can provide us with invaluable information about the choices of groups or individuals. In areas like e-commerce, which typically deal with decisions from thousands of users, the acquisition of preferences can be a difficult task. For these reasons, artificial intelligence (in particular, machine learning) methods have been increasingly important to the discovery and automatic learning of models about preferences. In this Ph.D. project, several approaches were analyzed and proposed to deal with the LR problem. Most of which has focused on pattern mining methods.Algorithms and the Foundations of Software technolog