Bayes rule based and decision tree hybrid classifier

Abstract

The present invention provides a hybrid classifier, called the NB-Tree classifier, for classifying a set of records. According to the present invention, the NB-Tree classifier includes a Decision-Tree structure having zero or more decision-nodes and one or more leaf-nodes. At each decision-node, a test is performed based on one or more attributes. At each leaf-node, a classifier based on Bayes Rule classifies the records. Furthermore, the present invention provides a method for inducing the NB-Tree classifier from a set of labeled instances. To induce the NB-Tree classifier, a utility C,of a Bayes classifier at a root-node is first estimated. Next, a utility D,of a split into a plurality of child-nodes with a Bayes classifier at the child-nodes is estimated. The utility of a split is the weighted sum of the utility of the child-nodes, where the weight given to a child-node is proportional to the number of instances that go down that child-node. Next, it is determined if C,is higher than D,. If C,is higher than D,, the root-node is transformed into a leaf-node with a Bayes classifier. If C,is not higher than D,, the root-node is transformed into a decision-node, and the instances are partitioned into a plurality of child-nodes. The method then recursively performs the previous steps for each child-node as if it is a root-node. The present invention approximates whether a generalization accuracy for a Naive-Bayes classifier at each leaf-node is higher than a single Naive-Bayes classifier at the decision-node. According to one embodiment of the present invention, to avoid splits with little value, a split is defined to be significant if the relative (not absolute) reduction in error is greater than 5% and there are at least 30 instances in the node.