International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Inferring decision trees using the minimum description length principle
Information and Computation
On the induction of decision trees for multiple concept learning
On the induction of decision trees for multiple concept learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data 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
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th 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
Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
IEEE Transactions on Knowledge and Data Engineering
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Prototype-based mining of numeric data streams
Proceedings of the 2003 ACM symposium on Applied computing
Building multi-way decision trees with numerical attributes
Information Sciences: an International Journal
Hierarchical Decision Tree Induction in Distributed Genomic Databases
IEEE Transactions on Knowledge and Data Engineering
An approach to mining the multi-relational imbalanced database
Expert Systems with Applications: An International Journal
Integrating in-process software defect prediction with association mining to discover defect pattern
Information and Software Technology
An empirical determination of samples for decision trees
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Expert Systems with Applications: An International Journal
An effective sampling method for decision trees considering comprehensibility and accuracy
WSEAS Transactions on Computers
Sampling scheme for better RBF network
Proceedings of the 2009 International Conference on Hybrid Information Technology
Class-oriented reduction of decision tree complexity
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Improving the performance of minor class in decision tree using duplicating instances
AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Ozone day prediction with radial basis function networks
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume II
Using reliable short rules to avoid unnecessary tests in decision trees
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
An evolutionary and attribute-oriented ensemble classifier
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
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
An Efficient Method for Discretizing Continuous Attributes
International Journal of Data Warehousing and Mining
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Classification is an important problem in data mining. Given a database of records, each with a class label, a classifier generates a concise and meaningful description for each class that can be used to classify subsequent records. A number of popular classifiers construct decision trees to generate class models. These classifiers first build a decision tree and then prune subtrees from the decision tree in a subsequent ipruning phase to improve accuracy and prevent “overfitting”.Generating the decision tree in two distinct phases could result in a substantial amount of wasted effort since an entire subtree constructed in the first phase may later be pruned in the next phase. In this paper, we propose PUBLIC, an improved decision tree classifier that integrates the second “pruning” phase with the initial “building” phase. In PUBLIC, a node is not expanded during the building phase, if it is determined that it will be pruned during the subsequent pruning phase. In order to make this determination for a node, before it is expanded, PUBLIC computes a lower bound on the minimum cost subtree rooted at the node. This estimate is then used by PUBLIC to identify the nodes that are certain to be pruned, and for such nodes, not expend effort on splitting them. Experimental results with real-life as well as synthetic data sets demonstrate the effectiveness of PUBLIC's integrated approach which has the ability to deliver substantial performance improvements.