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Relaxing Feature Selection in Spam Filtering by Using Case-Based Reasoning Systems
Progress in Artificial Intelligence
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.doi:10.1007/978-3-540-77002-2_5 dblp:conf/epia/MendezFGDC07 fatcat:lgcik3bvmvbrhmir6hjshw5veu