Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS

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  • Publication
    Quantum Feature Selection
    ( 2022-03-24)
    Mücke, Sascha
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    Heese, Raoul
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    Müller, Sabine
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    Wolter, Moritz
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    Piatkowski, Nico
    In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy. In contrast to iterative or greedy methods, our direct approach yields higher- quality solutions. QUBO problems are particularly interesting because they can be solved on quantum hardware. To evaluate our proposed algorithm, we conduct a series of numerical experiments using a classical computer, a quantum gate computer and a quantum annealer. Our evaluation compares our method to a range of standard methods on various benchmark datasets. We observe competitive performance.
  • Publication
    Informed Machine Learning - A Taxonomy and Survey of Integrating Knowledge into Learning Systems
    ( 2019-03-29)
    Rüden, Laura von
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    Mayer, Sebastian
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    Beckh, Katharina
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    Georgiev, Bogdan
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    Giesselbach, Sven
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    Heese, Raoul
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    Kirsch, Birgit
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    Pfrommer, Julius
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    Pick, Annika
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    Ramamurthy, Rajkumar
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    Schuecker, Jannis
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    Garcke, Jochen
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    Bauckhage, Christian
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    Walczak, Michal
    Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process, which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. First, we provide a definition and propose a concept for informed machine learning, which illustrates its building blocks and distinguishes it from conventional machine learning. Second, we introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Third, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.