Title

K-Norm Misclassification Rate Estimation For Decision Trees

Keywords

Decision tree; Law of succession; Pruning

Abstract

The decision tree classifier is a well-known methodology for classification. It is widely accepted that a fully grown tree is usually over-fit to the training data and thus should be pruned back. In this paper, we analyze the overtraining issue theoretically using an the k-norm risk estimation approach with Lidstone's Estimate. Our analysis allows the deeper understanding of decision tree classifiers, especially on how to estimate their misclassification rates using our equations. We propose a simple pruning algorithm based on our analysis and prove its superior properties, including its independence from validation and its efficiency.

Publication Date

12-1-2007

Publication Title

Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2007

Number of Pages

163-168

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

54949142836 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/54949142836

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