Information retrieval
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning decision tree classifiers
ACM Computing Surveys (CSUR)
An Exact Probability Metric for Decision Tree Splitting and Stopping
Machine Learning
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Classification and regression: money *can* grow on trees
KDD '99 Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Pattern Recognition and Image Processing
Pattern Recognition and Image Processing
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
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Families of splitting criteria for classification trees
Statistics and Computing
Machine Learning
On the quest for easy-to-understand splitting rules
Data & Knowledge Engineering
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Scalable Classification over SQL Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
ART: A Hybrid Classification Model
Machine Learning
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Information Sciences: an International Journal
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
International Journal of Remote Sensing
Building a cost-constrained decision tree with multiple condition attributes
Information Sciences: an International Journal
A hierarchical model for test-cost-sensitive decision systems
Information Sciences: an International Journal
A discretization algorithm for uncertain data
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Multi-Test decision trees for gene expression data analysis
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
Texture based decision tree classification for Arecanut
Proceedings of the CUBE International Information Technology Conference
A vector-valued support vector machine model for multiclass problem
Information Sciences: an International Journal
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Decision trees are probably the most popular and commonly used classification model. They are recursively built following a top-down approach (from general concepts to particular examples) by repeated splits of the training dataset. When this dataset contains numerical attributes, binary splits are usually performed by choosing the threshold value which minimizes the impurity measure used as splitting criterion (e.g. C4.5 gain ratio criterion or CART Gini's index). In this paper we propose the use of multi-way splits for continuous attributes in order to reduce the tree complexity without decreasing classification accuracy. This can be done by intertwining a hierarchical clustering algorithm with the usual greedy decision tree learning.