ComEnVprs: a novel approach for inducing decision tree classifiers

Authors:
Shuqin Wang;Jinmao Wei;Junping You;Dayou Liu
Affiliations:
School of Mathematics & Statistics, Northeast Normal University, Jilin, China;Institute of Computational Intelligence, Northeast Normal University, Jilin, China;Institute of Computational Intelligence, Northeast Normal University, Jilin, China;Open Symbol Computation and Knowledge Engineering Laboratory of State Education, Jilin University, Jilin, China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

Citing 12
Cited 3

Rough sets: probabilistic versus deterministic approach

International Journal of Man-Machine Studies
Inferring decision trees using the minimum description length principle

Information and Computation
Imprecise concept learning within a growing language

Proceedings of the sixth international workshop on Machine learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Variable precision rough set model

Journal of Computer and System Sciences
Learning belief networks from data: an information theory based approach

CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Data mining and rough set theory

Communications of the ACM
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets

Data Mining and Knowledge Discovery
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning

Data Mining and Knowledge Discovery
An Empirical Comparison of Selection Measures for Decision-Tree Induction

Machine Learning
Induction of Decision Trees

Machine Learning
On the quest for easy-to-understand splitting rules

Data & Knowledge Engineering

Decision trees: a recent overview

Artificial Intelligence Review
Variable precision rough set based decision tree classifier

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Hybrid approaches for approximate reasoning
A hybrid decision tree classifier

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new approach for inducing decision trees by combining information entropy criteria with VPRS based methods. From the angle of rough set theory, when inducing decision trees, entropy based methods emphasize the effect of class distribution. Whereas the rough set based approaches emphasize the effect of certainty. The presented approach takes the advantages of both criteria for inducing decision trees. Comparisons between the presented approach and the fundamental information entropy based method on some data sets from the UCI Machine Learning Repository are also reported.