Decision Tree Toolkit: A Component-Based Library of Decision Tree Algorithms

Authors:
Nikos Drossos;Athanassios Papagelis;Dimitrios Kalles
Affiliations:
-;-;-
Venue:
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Year:
2000

Citing 6
Cited 1

Instance-Based Learning Algorithms

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Induction of Decision Trees

Machine Learning
The Power of Decision Tables

ECML '95 Proceedings of the 8th European Conference on Machine Learning
Estimating continuous distributions in Bayesian classifiers

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

Component-based decision trees for classification

Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper reports on the development of a library of decision tree algorithms in Java. The basic model of a decision tree algorithm is presented and then used to justify the design choices and system architecture issues. The library has been designed for flexibility and adaptability. Its basic goal was an open system that could easily embody parts of different conventional as well as new algorithms, without the need of knowing the inner organization of the system in detail. The system has an integrated interface (ClassExplorer), which is used for controlling and combining components that comprise decision trees. The ClassExplorer can create objects "on the fly", from classes unknown during compilation time. Conclusions and considerations about extensions towards a more visual system are also described.