Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
395842 | Information Sciences | 2009 | 11 Pages |
Abstract
Motivated by the desire to construct compact (in terms of expected length to be traversed to reach a decision) decision trees, we propose a new node splitting measure for decision tree construction. We show that the proposed measure is convex and cumulative and utilize this in the construction of decision trees for classification. Results obtained from several datasets from the UCI repository show that the proposed measure results in decision trees that are more compact with classification accuracy that is comparable to that obtained using popular node splitting measures such as Gain Ratio and the Gini Index.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
B. Chandra, P. Paul Varghese,