User-Centric Active Learning for Outlier Detection

Outlier detection searches for unusual, rare observations in large, often high-dimensional data sets. One of the fundamental challenges of outlier detection is that ``unusual\u27\u27 typically depends on the perception of a user, the recipient of the detection result. This makes finding a formal definition of ``unusual\u27\u27 that matches with user expectations difficult. One way to deal with this issue is active learning, i.e., methods that ask users to provide auxiliary information, such as class label annotations, to return algorithmic results that are more in line with the user input. Active learning is well-suited for outlier detection, and many respective methods have been proposed over the last years. However, existing methods build upon strong assumptions. One example is the assumption that users can always provide accurate feedback, regardless of how algorithmic results are presented to them -- an assumption which is unlikely to hold when data is high-dimensional. It is an open question to which extent existing assumptions are in the way of realizing active learning in practice. In this thesis, we study this question from different perspectives with a differentiated, user-centric view on active learning. In the beginning, we structure and unify the research area on active learning for outlier detection. Specifically, we present a rigorous specification of the learning setup, structure the basic building blocks, and propose novel evaluation standards. Throughout our work, this structure has turned out to be essential to select a suitable active learning method, and to assess novel contributions in this field. We then present two algorithmic contributions to make active learning for outlier detection user-centric. First, we bring together two research areas that have been looked at independently so far: outlier detection in subspaces and active learning. Subspace outlier detection are methods to improve outlier detection quality in high-dimensional data, and to make detection results more easy to interpret. Our approach combines them with active learning such that one can balance between detection quality and annotation effort. Second, we address one of the fundamental difficulties with adapting active learning to specific applications: selecting good hyperparameter values. Existing methods to estimate hyperparameter values are heuristics, and it is unclear in which settings they work well. In this thesis, we therefore propose the first principled method to estimate hyperparameter values. Our approach relies on active learning to estimate hyperparameter values, and returns a quality estimate of the values selected. In the last part of the thesis, we look at validating active learning for outlier detection practically. There, we have identified several technical and conceptual challenges which we have experienced firsthand in our research. We structure and document them, and finally derive a roadmap towards validating active learning for outlier detection with user studies

Validating one-class active learning with user studies – A prototype and open challenges

Active learning with one-class classifiers involves users in the detection of outliers. The evaluation of one-class active learning typically relies on user feedback that is simulated, based on benchmark data. This is because validations with real users are elaborate. They require the de-sign and implementation of an interactive learning system. But without such a validation, it is unclear whether the value proposition of active learning does materialize when it comes to an actual detection of out-liers. User studies are necessary to find out when users can indeed provide feedback. In this article, we describe important characteristics and pre-requisites of one-class active learning for outlier detection, and how they influence the design of interactive systems. We propose a reference architecture of a one-class active learning system. We then describe design alternatives regarding such a system and discuss conceptual and technical challenges. We conclude with a roadmap towards validating one-class active learning with user studies

What do algorithms explain? The issue of the goals and capabilities of Explainable Artificial Intelligence (XAI)

The increasing ubiquity of machine learning (ML) motivates research on algorithms to “explain” models and their predictions—so-called Explainable Artificial Intelligence (XAI). Despite many publications and discussions, the goals and capabilities of such algorithms are far from being well understood. We argue that this is because of a problematic reasoning scheme in the literature: Such algorithms are said to complement machine learning models with desired capabilities, such as interpretability or explainability. These capabilities are in turn assumed to contribute to a goal, such as trust in a system. But most capabilities lack precise definitions and their relationship to such goals is far from obvious. The result is a reasoning scheme that obfuscates research results and leaves an important question unanswered: What can one expect from XAI algorithms? In this paper, we clarify the modest capabilities of these algorithms from a concrete perspective: that of their users. We show that current algorithms can only answer user questions that can be traced back to the question: “How can one represent an ML model as a simple function that uses interpreted attributes?”. Answering this core question can be trivial, difficult or even impossible, depending on the application. The result of the paper is the identification of two key challenges for XAI research: the approximation and the translation of ML models

Towards Simulation-Data Science : A Case Study on Material Failures

Simulations let scientists study properties of complex systems. At first sight, data mining is a good choice when evaluating large numbers of simulations. But it is currently unclear whether there are general principles that might guide the deployment of respective methods to simulation data. In other words, is it worthwhile to target at “simulation-data science” as a distinct subdiscipline of data science? To identify a respective research agenda and to structure the research questions, we conduct a case study from the domain of materials science. One insight that simulation data may be different from other data regarding its structure and quality, which entails focal points different from the ones of conventional data-analysis projects. It also turns out that interpretability and usability are important notions in our context as well. More attention is needed to gather the various meanings of these terms to align them with the needs and priorities of domain scientists. Finally, we propose extensions to our case study which we deem necessary to generalize our insights towards the guidelines envisioned for “simulation-data science”

An Empirical Evaluation of Constrained Feature Selection

While feature selection helps to get smaller and more understandable prediction models, most existing feature-selection techniques do not consider domain knowledge. One way to use domain knowledge is via constraints on sets of selected features. However, the impact of constraints, e.g., on the predictive quality of selected features, is currently unclear. This article is an empirical study that evaluates the impact of propositional and arithmetic constraints on filter feature selection. First, we systematically generate constraints from various types, using datasets from different domains. As expected, constraints tend to decrease the predictive quality of feature sets, but this effect is non-linear. So we observe feature sets both adhering to constraints and with high predictive quality. Second, we study a concrete setting in materials science. This part of our study sheds light on how one can analyze scientific hypotheses with the help of constraints