Abstract
Classifiers can be used to automatically dispatch the abundance of newly created documents to recipients interested in particular topics. Identification of adequate training examples is essential for classification performance, but it may prove to be a challenging task in large document repositories. We propose a classifier hypothesis generation method relying on automated analysis and information visualisation. In our approach visualisations are used to explore the document sets and to inspect the results of machine learning methods, allowing the user to assess the classifier performance and adapt the classifier by gradually refining the training set.
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Seifert, C., Sabol, V., Granitzer, M. (2010). Classifier Hypothesis Generation Using Visual Analysis Methods. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14292-5_11
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DOI: https://doi.org/10.1007/978-3-642-14292-5_11
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