Title

Properties Of The K-Norm Pruning Algorithm For Decision Tree Classifiers

Abstract

Pruning is one of the key procedures in training decision tree classifiers. It removes trivial rules from the raw knowledge base built from training examples, in order to avoid over-using noisy, conflicting, or fuzzy inputs, so that the refined model can generalize better with unseen cases. In this paper, we present a number of properties of k-norm pruning, a recently proposed pruning algorithm, which has clear theoretical interpretation. In an earlier paper it was shown that k-norm pruning compares very favorably in terms of accuracy and size with Minimal Cost-Complexity Pruning and Error Based Pruning, two ofthe most cited decision tree pruning methods; it was also shown that k-norm pruning is llzore efficient, at times orders of magnitude more efficient than Minimal Cost-Complexity Pruning and Error Based Pruning. In this paper, we demonstrate the validity ofthe k-norm properties through a series of theorel11s, and explain their practical significance. © 2008 IEEE.

Publication Date

1-1-2008

Publication Title

Proceedings - International Conference on Pattern Recognition

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/icpr.2008.4761277

Socpus ID

77957940083 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77957940083

This document is currently not available here.

Share

COinS