Generalized conditional entropy and a metric splitting criterion for decision trees

Authors:
Dan A. Simovici;Szymon Jaroszewicz
Affiliations:
Dept. of Computer Science, University of Massachusetts at Boston, Boston, Massachusetts;Faculty of Computer and Information Systems, Technical University of Szeczin, Poland
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

Citing 5
Cited 0

A Distance-Based Attribute Selection Measure for Decision Tree Induction

Machine Learning
Metric Incremental Clustering of Nominal Data

ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
An axiomatization of partition entropy

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

We examine a new approach to building decision tree by introducing a geometric splitting criterion, based on the properties of a family of metrics on the space of partitions of a finite set. This criterion can be adapted to the characteristics of the data sets and the needs of the users and yields decision trees that have smaller sizes and fewer leaves than the trees built with standard methods and have comparable or better accuracy.