BSNBC classifying algorithm measured by conditional mutual information
Zongtian Liu · Jisuanji yingyong yanjiu · 2007
This paper put forward a new learning algorithm of BSNBC based on integrated consideration of algorithm efficiency and efficacy.The traditional SNBC algorithm could only combine two basic attributes to form a combined attribute,which restricted SNBC's classifying performance greatly.To a certain extent,BSNBC could overcome such weakness,it could combine no more than K basic attributes into a combined attribute.Integer programming(IP) and linear programming(LP) methods could be used to learn BSNBC,but both their searching processes had some blindness.This algorithm had the capability of joining attributes with close relationship into a combined attribute by conditional mutual entropy.The experimental results show its efficiency.