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An improved Bayesian filtering technique for spam recognition

Authors: ADIO Adesina

Publication Type: Journal article

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Abstract

Abstract : In this paper, we presented models and software for spam recognition using an impro ved Bayesian filtering technique. Based on a corpus from Androutsopoulos et al, our Spam Recognition framework outperforms other state -of-the-art learning methods based on Bayesian algorithm in terms of spam detection capability. Our software has proved an accuracy of 99. 9% of good classification. The 0.1% of other messages have been classify as â€œmay be spamâ€ due to their vagueness signature. Brief, in the case of extremely high mis-classification cost, our model still remains stable accuracy with low computation cost, while other methodsâ€™ performance deteriorates significantly as the cost factor increases.

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ADIO,A. . (0000). An improved Bayesian filtering technique for spam recognition, 0 (), 0-0.

ADIO,A. . "An improved Bayesian filtering technique for spam recognition" 0, no (), (0000): 0-0.

ADIO,A. and . (0000). An improved Bayesian filtering technique for spam recognition, 0 (), pp0-0.

ADIOA, . An improved Bayesian filtering technique for spam recognition. 0000, 0 ():0-0.

ADIO,Adesina , . "An improved Bayesian filtering technique for spam recognition", 0 . (0000) : 0-0.

A.Adesina , "An improved Bayesian filtering technique for spam recognition" vol.0, no., pp. 0-0, 0000.

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