Relaxing Feature Selection in Spam Filtering by Using Case-Based Reasoning Systems
Fecha de publicación
Springer Science + Business Media
Progress in Artificial Intelligence. 13th Portuguese Conference on Aritficial Intelligence, EPIA 2007, Workshops: GAIW, AIASTS, ALEA, AMITA, BAOSW, BI, CMBSB, IROBOT, MASTA, STCS, and TEMA, Guimarães, Portugal, December 3-7, 2007. Proceedings. Lecture Notes in Computer Science. Volumen 4874, pp. 53-62.
This paper presents a comparison between two alternative strategies for addressing feature selection on a well known case-based reasoning spam filtering system called SpamHunting. We present the usage of the k more predictive features and a percentage-based strategy for the exploitation of our amount of information measure. Finally, we confirm the idea that the percentage feature selection method is more adequate for spam filtering domain.
978-3-540-77002-2 (Online) / 978-3-540-77000-8 (Print)
0302-9743 (Print) / 1611-3349 (Online)
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