Abstract

Lbr is a lazy semi-naive Bayesian classifier learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classification. To classify a test example, it creates a conjunctive rule that selects a most appropriate subset of training examples and induces a local naive Bayesian classifier using this subset. Lbr can significantly improve the performance of the naive Bayesian classifier. A bias and variance analysis of Lbr reveals that it significantly reduces the bias of naive Bayesian classification at a cost of a slight increase in variance. It is interesting to compare this lazy technique with boosting and bagging, two well-known state-of-the-art non-lazy learning techniques. Empirical comparison of Lbr with boosting decision trees on discrete valued data shows that Lbr has, on average, significantly lower variance and higher bias. As a result of the interaction of these effects, the average prediction error of Lbr over a range of learning tasks is at...

Citations