首页|School of Business Reports Findings in Machine Learning (Machine learning miscla ssification networks reveal a citation advantage of interdisciplinary publicatio ns only in high-impact journals)
School of Business Reports Findings in Machine Learning (Machine learning miscla ssification networks reveal a citation advantage of interdisciplinary publicatio ns only in high-impact journals)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Bremen, Germ any, by NewsRx correspondents, research stated, "Given a large enough volume of data and precise, meaningful categories, training a statistical model to solve a classification problem is straightforward and has become a standard application of machine learning (ML). If the categories are not precise, but rather fuzzy, as in the case of scientific disciplines, the systematic failures of ML classifi cation can be informative about properties of the underlying categories." Financial support for this research came from Constructor University Bremen gGmb H. Our news editors obtained a quote from the research from the School of Business, "Here we classify a large volume of academic publications using only the abstra ct as information. From the publications that are classified differently by jour nal categories and ML categories (i.e., misclassified publications, when using the journal assignment as ground truth) we construct a network among disciplines. Analysis of these misclassifications provides insight in two topics at the core of the science of science: (1) Mapping out the interplay of disciplines. We sho w that this misclassification network is informative about the interplay of acad emic disciplines and it is similar to, but distinct from, a citation-based map o f science, where nodes are scientific disciplines and an edge indicates a strong co-citation count between publications in these disciplines. (2) Analyzing the success of interdisciplinarity."
BremenGermanyEuropeCyborgsEmergi ng TechnologiesMachine Learning
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