Abstract
Semi-supervised learning requires some data to be labeled but then uses this in conjunction with a large amount of unlabeled data to learn a model for a domain. Since the labeled data should be representative of the range of unlabeled data available, the aim of this research is to identify which data should be labeled. An approach has been developed where a domain expert starts to label unlabeled data and also writes rules to classify such data. The labeled data are also used as machine learning training data. If the expert rules and the rules developed by machine learning agree on a label for an unseen datum, the label is accepted and the case automatically added to the training data for learning, otherwise the case is checked by the expert and if the label from the rules is wrong, the expert provides the correct label and a rule to correctly classify the case. Further data is then processed in the same way. Results from a number of datasets using a simulated expert as the domain expert suggest that this method produces more accurate knowledge bases than other semi-supervised methods using similar amounts of labeled data and the resultant knowledge bases are as accurate as having all the data labeled.
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Finlayson, A., Compton, P. (2014). Using a Domain Expert in Semi-supervised Learning. In: Kim, Y.S., Kang, B.H., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2014. Lecture Notes in Computer Science(), vol 8863. Springer, Cham. https://doi.org/10.1007/978-3-319-13332-4_9
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DOI: https://doi.org/10.1007/978-3-319-13332-4_9
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