Intermediate decision trees

Authors:
Lawrence B. Holder
Affiliations:
Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX
Venue:
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Year:
1995

Citing 6
Cited 4

Boolean Feature Discovery in Empirical Learning

Machine Learning
The general utility problem in machine learning

Proceedings of the seventh international conference (1990) on Machine learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Overfitting Avoidance as Bias

Machine Learning
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

Machine Learning
An Empirical Comparison of Pruning Methods for Decision Tree Induction

Machine Learning

The Biases of Decision Tree Pruning Strategies

IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Simplifying decision trees: A survey

The Knowledge Engineering Review
A data mining approach for heavy rainfall forecasting based on satellite image sequence analysis

Computers & Geosciences
Improved decision tree induction: Prioritized Height Balanced tree with entropy to find hidden rules

Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Intermediate decision trees are the subtrees of the full (unpruned) decision tree generated in a breadth-first order. An extensive empirical investigation evaluates the classification error of intermediate decision trees and compares their performance to full and pruned trees. Empirical results were generated using C4.5 with 66 databases from the UCI machine learning database repository Results show that when attempting to minimize the error of the pruned tree produced by C4.5, the best intermediate tree performs significantly better in 46 of the 66 databases. These and other results question the effectiveness of decision tree pruning strategies and suggest further consideration of the full tree and its intermediates. Also, the results reveal specific properties satisfied by databases in which the intermediate full tree performs best Such relationships improve guidelines for selecting appropriate inductive strategies based on domain properties.