IRISA Participation in JRS 2012 Data-Mining Challenge: Lazy-Learning with Vectorization

Vincent Claveau 1
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
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 Informa- tion 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 Lan- guage Processing frameworks. The resulting system is simple and efficient while offering good performance.
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Contributor : Vincent Claveau <>
Submitted on : Monday, December 3, 2012 - 3:15:50 PM
Last modification on : Friday, November 16, 2018 - 1:25:10 AM
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  • HAL Id : hal-00760145, version 1


Vincent Claveau. IRISA Participation in JRS 2012 Data-Mining Challenge: Lazy-Learning with Vectorization. JRS - Data Mining Competition: Topical Classification of Biomedical Research Papers, special event of Joint Rough Sets Symposium, Sep 2012, Chengdu, China. ⟨hal-00760145⟩



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