Induction of linear decision trees with real-coded genetic algorithms and k-d trees

Authors:
Sai-cheong Ng;Kwong-sak Leung
Affiliations:
The Chinese University of Hong Kong;The Chinese University of Hong Kong
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

Citing 8
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis

Artificial Intelligence Review
Multidimensional binary search trees used for associative searching

Communications of the ACM
Oblique Linear Tree

IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Multivariate Decision Trees

Multivariate Decision Trees
A system for induction of oblique decision trees

Journal of Artificial Intelligence Research
Inducing oblique decision trees with evolutionary algorithms

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Although genetic algorithm-based decision tree algorithms are applied successfully in various classification tasks, their execution times are quite long on large datasets. A novel decision tree algorithm, called Real-Coded Genetic Algorithm-based Linear Decision Tree Algorithm with k-D Trees (RCGA-based LDT with kDT), is proposed. In the proposed algorithm, a k-D tree is built when a new node of a linear decision tree is created. The use of k-D trees speeds up the construction of linear decision trees without sacrificing the quality of the constructed decision trees.