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Prevent Low-Quality Analytics by Automatic Selection of the Best-Fitting Training Data

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dc.contributor.author Kiefer, Cornelia
dc.contributor.author Reimann, Peter
dc.contributor.author Mitschang, Bernhard
dc.date.accessioned 2020-01-04T07:21:27Z
dc.date.available 2020-01-04T07:21:27Z
dc.date.issued 2020-01-07
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63868
dc.description.abstract Data analysis pipelines consist of a sequence of various analysis tools. Most of these tools are based on supervised machine learning techniques and thus rely on labeled training data. Selecting appropriate training data has a crucial impact on analytics quality. Yet, most of the times, domain experts who construct analysis pipelines neglect the task of selecting appropriate training data. They rely on default training data sets, e.g., since they do not know which other training data sets exist and what they are used for. Yet, default training data sets may be very different from the domain-specific input data that is to be analyzed, leading to low-quality results. Moreover, these input data sets are usually unlabeled. Thus, information on analytics quality is not measurable with evaluation metrics. Our contribution comprises a method that (1) indicates the expected quality to the domain expert while constructing the analysis pipeline, without need for labels and (2) automatically selects the best-fitting training data. It is based on a measurement of the similarity between input and training data. In our evaluation, we consider the part-of-speech tagger tool and show that Latent Semantic Analysis (LSA) and Cosine Similarity are suited as indicators for the quality of analysis results and as basis for an automatic selection of the best-fitting training data.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data, Text, and Web Mining for Business Analytics
dc.subject data quality
dc.subject domain-specific data analysis
dc.subject text analysis
dc.subject text similarity
dc.subject training data
dc.title Prevent Low-Quality Analytics by Automatic Selection of the Best-Fitting Training Data
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2020.129
Appears in Collections: Data, Text, and Web Mining for Business Analytics


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