Abstract
We propose in this paper an adaptation of the k-Nearest Neighbor (k-NN) algorithm using category specific thresholds in a multiclass environment where a document can belong to more than one class. Our method uses feedback to tune the thresholds and in turn the classification performance over time. The experiments were run on the InFile data, comprising 100,000 English documents and 50 topics.
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Bodinier, V., Qamar, A.M., Gaussier, E. (2009). Online Document Filtering Using Adaptive k-NN. In: Peters, C., et al. Evaluating Systems for Multilingual and Multimodal Information Access. CLEF 2008. Lecture Notes in Computer Science, vol 5706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04447-2_126
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DOI: https://doi.org/10.1007/978-3-642-04447-2_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04446-5
Online ISBN: 978-3-642-04447-2
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