Christian Beyer

Christian Beyer
Otto-von-Guericke-Universität Magdeburg | OvGU

MS Science

About

10
Publications
1,204
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40
Citations
Introduction
I am Christian and very interested in using data mining for ethical purposes and helping people. My main interests are currently active learning, social networks and natural language processing.
Additional affiliations
October 2013 - January 2015
Otto-von-Guericke-Universität Magdeburg
Position
  • Master's Student

Publications

Publications (10)
Article
Full-text available
Monitoring the immune system’s status has emerged as an urgent demand in critical health conditions. The circulating cytokine levels in the blood reflect a thorough insight into the immune system status. Indeed, measuring one cytokine may deliver more information equivalent to detecting multiple diseases at a time. However, if the reported cytokine...
Article
Full-text available
Many current and future applications plan to provide entity-specific predictions. These range from individualized healthcare applications to user-specific purchase recommendations. In our previous stream-based work on Amazon review data, we could show that error-weighted ensembles that combine entity-centric classifiers, which are only trained on r...
Article
Full-text available
Traditional active learning tries to identify instances for which the acquisition of the label increases model performance under budget constraints. Less research has been devoted to the task of actively acquiring feature values, whereupon both the instance and the feature must be selected intelligently and even less to a scenario where the instanc...
Chapter
Full-text available
Missing data is a common occurrence in the time series domain, for instance due to faulty sensors, server downtime or patients not attending their scheduled appointments. One of the best methods to impute these missing values is Multiple Imputations by Chained Equations (MICE) which has the drawback that it can only model linear relationships among...
Article
Full-text available
Stream classification algorithms traditionally treat arriving instances as independent. However, in many applications, the arriving examples may depend on the “entity” that generated them, e.g. in product reviews or in the interactions of users with an application server. In this study, we investigate the potential of this dependency by partitionin...
Conference Paper
Opinion stream classification algorithms adapt the model to the arriving review texts and, depending on the forgetting scheme, reduce the contribution old reviews have upon the model. Reviews are assumed independent, and information on the entity to which a review refers, i.e. to the opinion target, is thereby ignored. This implies that the predict...
Conference Paper
Opinion stream mining algorithms learn and adapt a polarity model as new opinionated texts arrive. Text understanding is computationally expensive though, and sensitive to the emergence of new words. In this work, we study polarity prediction for opinions on given entities and investigate how prediction quality is affected when we ignore the text o...
Conference Paper
Full-text available
Facing ever increasing volumes of data but limited human annotation capabilities, active learning strategies for selecting the most informative labels gain in importance. However, the choice of an appropriate active learning strategy itself is a complex task that requires to consider different criteria such as the informativeness of the selected la...
Conference Paper
Full-text available
Facing ever increasing volumes of data but limited human annotation capabilities, active learning strategies for selecting the most informative labels gain in importance. However, the choice of an appropriate active learning strategy itself is a complex task that requires to consider different criteria such as the informativeness of the selected la...

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Projects

Projects (2)
Project
Active Learning addresses the intersection between Data Mining/Machine Learning and interaction with humans. Aiming at optimizing this interaction, it bridges the gap between data-centric and user-centric approaches. For example, by requesting the most relevant information or performing the most informative experiment. Facing big volumes of data but limited human annotation and supervision capacities, active approaches become increasingly important for improving the efficiency in interactions.