Combining text and heuristics for cost-sensitive spam filtering
José María Gómez Hidalgo, Manuel Maña López, Enrique Puertas Sanz · 2000
Spam filtering is a text categorization task that shows especial features that make it interesting and difficult.First, the task has been performed traditionally using heuristics from the domain.Second, a cost model is required to avoid misclassification of legitimate messages.We present a comparative evaluation of several machine learning algorithms applied to spam filtering, considering the text of the messages and a set of heuristics for the task.Cost-oriented biasing and evaluation is performed.