C4.5: programs for machine learning
C4.5: programs for machine learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Towards scalable support vector machines using squashing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Induction By Attribute Elimination
IEEE Transactions on Knowledge and Data Engineering
IEEE Intelligent Systems
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Bias Correction in Classification Tree Construction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Multi-interval Discretization Methods for Decision Tree Learning
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Classification-Hierarchy Table: A Methodology for Constructing the Classification Tree
ASWEC '96 Proceedings of the 1996 Australian Software Engineering Conference
A New Restructuring Algorithm for the Classification-Tree Method
STEP '99 Proceedings of the Software Technology and Engineering Practice
IEEE Transactions on Knowledge and Data Engineering
Building multi-way decision trees with numerical attributes
Information Sciences: an International Journal
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Bundling classifiers by bagging trees
Computational Statistics & Data Analysis
What should be minimized in a decision tree?
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Building a cost-constrained decision tree with multiple condition attributes
Information Sciences: an International Journal
Information Sciences: an International Journal
A GRASP method for building classification trees
Expert Systems with Applications: An International Journal
Model selection in omnivariate decision trees using Structural Risk Minimization
Information Sciences: an International Journal
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We propose a simple heuristic partition method (HPM) of classification tree to improve efficiency in the search for splitting points of numerical attributes. The proposal is motivated by the idea that the selection process of candidates in the splitting point selection can be made more flexible as to achieve a faster computation while retaining classification accuracy. We compare the performance of the HPM against Fayyad's method, as the latter is the improved version of the standard C4.5 algorithm on the search of splitting points. We demonstrate that HPM is more efficient, in some cases by as much as 50%, while producing essentially the same classification for six different data sets. Our result supports the relaxation of instance boundaries (RIB) as a valid approach that can be explored to achieve more efficient computations.