C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine Learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Parallel Formulations of Decision-Tree Classification Algorithms
Data Mining and Knowledge Discovery
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
Data Mining and Knowledge Discovery
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Families of splitting criteria for classification trees
Statistics and Computing
IEEE Transactions on Knowledge and Data Engineering
Multiclass Alternating Decision Trees
ECML '02 Proceedings of the 13th European Conference on 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
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Optimizing the Induction of Alternating Decision Trees
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Instability of decision tree classification algorithms
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Tree Induction for Probability-Based Ranking
Machine Learning
Inference for the Generalization Error
Machine Learning
Machine Learning
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
Machine Learning
Parallel univariate decision trees
Pattern Recognition Letters
International Journal of Hybrid Intelligent Systems
A co-evolving decision tree classification method
Expert Systems with Applications: An International Journal
Induction of multiclass multifeature split decision trees from distributed data
Pattern Recognition
A CBR-based fuzzy decision tree approach for database classification
Expert Systems with Applications: An International Journal
A scalable decision tree system and its application in pattern recognition and intrusion detection
Decision Support Systems
A hybrid SVM based decision tree
Pattern Recognition
Applying Bayesian approach to decision tree
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
ComEnVprs: a novel approach for inducing decision tree classifiers
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
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Decision tree techniques have been widely used to build classification models. In this study, we attempted to increase the prediction accuracy of a decision tree model by integrating local application of Naive Bayes classifier. We performed a large-scale comparison with other state-of-the-art algorithms on 30 standard benchmark datasets and the proposed method gave statistical better accuracy in some cases.