Abstract
In this article, we report on our participation in the JRS Data-Mining Challenge. The approach used by our system is a lazy-learning one, based on a simple k-nearest-neighbors technique. We more specifically addressed this challenge as an opportunity to test Information Retrieval (IR) inspired techniques in such a data-mining framework. In particular, we tested different similarity measures, including one called vectorization that we have proposed and tested in IR and Natural Language Processing frameworks. The resulting system is simple and efficient while offering good performance.
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Claveau, V. (2012). IRISA Participation in JRS 2012 Data-Mining Challenge: Lazy-Learning with Vectorization. In: Yao, J., et al. Rough Sets and Current Trends in Computing. RSCTC 2012. Lecture Notes in Computer Science(), vol 7413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32115-3_53
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DOI: https://doi.org/10.1007/978-3-642-32115-3_53
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