C4.5: programs for machine learning
C4.5: programs for machine learning
Improving greedy algorithms by lookahead-search
Journal of Algorithms
Technical note: some properties of splitting criteria
Machine Learning
A New Criterion in Selection and Discretization of Attributes for the Generation of Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the well-behavedness of important attribute evaluation functions
SCAI '97 Proceedings of the sixth Scandinavian conference on Artificial intelligence
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
Algorithms for Finding Attribute Value Group for Binary Segmentation of Categorical Databases
IEEE Transactions on Knowledge and Data Engineering
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
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Elegant Decision Tree Algorithm for Classification in Data Mining
WISEW '02 Proceedings of the Third International Conference on Web Information Systems Engineering (Workshops) - (WISEw'02)
CMP: A Fast Decision Tree Classifier Using Multivariate Predictions
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
An Improved Attribute Selection Measure for Decision Tree Induction
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Lookahead and pathology in decision tree induction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Look-ahead based fuzzy decision tree induction
IEEE Transactions on Fuzzy Systems
Classifiability-based omnivariate decision trees
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Model selection in omnivariate decision trees using Structural Risk Minimization
Information Sciences: an International Journal
Information Sciences: an International Journal
A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Credit risk assessment and decision making by a fusion approach
Knowledge-Based Systems
Information Sciences: an International Journal
Decision trees: a recent overview
Artificial Intelligence Review
A case study of muscle dysmorphia disorder diagnostics
Expert Systems with Applications: An International Journal
Software effort prediction: a hyper-heuristic decision-tree based approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Automatic design of decision-tree algorithms with evolutionary algorithms
Evolutionary Computation
Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks
Expert Systems with Applications: An International Journal
Finite sets of data compatible with multidimensional inequality measures
Information Sciences: an International Journal
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Motivated by the desire to construct compact (in terms of expected length to be traversed to reach a decision) decision trees, we propose a new node splitting measure for decision tree construction. We show that the proposed measure is convex and cumulative and utilize this in the construction of decision trees for classification. Results obtained from several datasets from the UCI repository show that the proposed measure results in decision trees that are more compact with classification accuracy that is comparable to that obtained using popular node splitting measures such as Gain Ratio and the Gini Index.