PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning

Classification is an important problem in data mining. Given a database of records, each with a class label, a classifier generates a concise and meaningful description for each class that can be used to classify subsequent records. A number of popular classifiers construct decision trees to generate class models. These classifiers first build a decision tree and then prune subtrees from the decision tree in a subsequent pruning phase to improve accuracy and prevent "overfitting". In this paper, we propose PUBLIC, an improved decision tree classifier that integrates the second "pruning" phase with the initial "building" phase. In PUBLIC, a node is not expanded during the building phase, if it is determined that it will be pruned during the subsequent pruning phase. In order to make this determination for a node, before it is expanded, PUBLIC computes a lower bound on the minimum cost subtree rooted at the node. This estima.te is then used by PUBLIC to ide...

• Type: Conference
• Year: 1998
• Where: VLDB
• Authors: Rajeev Rastogi, Kyuseok Shim
• Keywords: Database, Decision Tree, Decision Tree Classifier, Subsequent Pruning Phase, VLDB 1998