mail/OSBF-lua 2.0.4nb6 NetBSD

OSBF-Lua (Orthogonal Sparse Bigrams with confidence Factor) is a Lua C module for text classification. It is a port of the OSBF classifier implemented in the CRM114 project. This implementation attempts to put focus on the classification task itself by using Lua as the scripting language, a powerful yet light-weight and fast language, which makes it easier to build and test more elaborated filters and training methods.

The OSBF algorithm is a typical Bayesian classifier but enhanced with two techniques originally developed for the CRM114 project: Orthogonal Sparse Bigrams - OSB, for feature extraction, and Exponential Differential Document Count - EDDC (a.k.a Confidence Factor), for automatic feature selection. Combined, these two techniques produce a highly accurate classifier. OSBF was developed focused on two classes, SPAM and NON-SPAM, so the performance for more than two classes may not be the same.

spamfilter.lua is an anti-spam filter written in Lua using the OSBF-lua module. It takes special advantage of EDDC to introduce TONE-HR, a highly effective training method. The combination of OSB, EDDC and TONE-HR to enhance a classical Bayesian classifier resulted in the best spam filtering performance in TREC's Spam Track 2006 and the CEAS 2008 Live Spam Filter Challenge.