Example – filtering mobile phone spam with the Naive Bayes algorithm
As the worldwide use of mobile phones has grown, a new avenue for electronic junk mail has opened for disreputable marketers. These advertisers utilize short message service (SMS) text messages to target potential consumers with unwanted advertising known as SMS spam. This type of spam is troublesome because, unlike email spam, an SMS message is particularly disruptive, due to the omnipresence of one's mobile phone. Developing a classification algorithm that could filter SMS spam would provide a useful tool for cellular phone providers.
Since Naive Bayes has been used successfully for email spam filtering, it seems likely that it could also be applied to SMS spam. However, relative to email spam, SMS spam poses additional challenges for automated filters. SMS messages are often limited to 160 characters, reducing the amount of text that can be used to identify whether a message is junk. The limit, combined with small mobile...
