Ant colony decision forest meta-ensemble

Authors:
Urszula Boryczka;Jan Kozak
Affiliations:
Institute of Computer Science, University of Silesia, Sosnowiec, Poland;Institute of Computer Science, University of Silesia, Sosnowiec, Poland
Venue:
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
Year:
2012

Citing 7
Cited 0

Designing Storage Efficient Decision Trees

IEEE Transactions on Computers
Bagging predictors

Machine Learning
Random Forests

Machine Learning
cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes

ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Data Mining with Decision Trees: Theroy and Applications

Data Mining with Decision Trees: Theroy and Applications
Ant colony decision trees: a new method for constructing decision trees based on ant colony optimization

ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
An adaptive discretization in the ACDT algorithm for continuous attributes

ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is devoted to the study of an extension of Ant Colony Decision Tree (ACDT) approach to Random Forests (RF) --- an arisen meta-ensemble technique called Ant Colony Decision Forest (ACDF). To the best of our knowledge this is the first time that Ant Colony Optimization is being applied as an ensemble method in data mining tasks. Meta-ensemble ACDF as a hybrid RF and ACO based algorithm is evolved and experimentally shown high accuracy and good effectiveness of this technique motivate us to further development.