C4.5: programs for machine learning
C4.5: programs for machine learning
Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
Pattern Recognition Letters
The application of AdaBoost for distributed, scalable and on-line learning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The distributed boosting algorithm
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Boosting Algorithms for Parallel and Distributed Learning
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Goal-Directed Classification Using Linear Machine Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Distributed learning with bagging-like performance
Pattern Recognition Letters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Relational Concepts and the Fourier Transform: An Empirical Study
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Binary Classification Trees for Multi-class Classification Problems
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Knowledge and Data Engineering
Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
International Journal of Hybrid Intelligent Systems
A comparison of generalized linear discriminant analysis algorithms
Pattern Recognition
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
New algorithms for learning and pruning oblique decision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent Decision Technologies
Decision trees: a recent overview
Artificial Intelligence Review
Building fast decision trees from large training sets
Intelligent Data Analysis
A hybrid decision tree classifier
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The decision tree-based classification is a popular approach for pattern recognition and data mining. Most decision tree induction methods assume training data being present at one central location. Given the growth in distributed databases at geographically dispersed locations, the methods for decision tree induction in distributed settings are gaining importance. This paper describes one such method that generates compact trees using multifeature splits in place of single feature split decision trees generated by most existing methods for distributed data. Our method is based on Fisher's linear discriminant function, and is capable of dealing with multiple classes in the data. For homogeneously distributed data, the decision trees produced by our method are identical to decision trees generated using Fisher's linear discriminant function with centrally stored data. For heterogeneously distributed data, a certain approximation is involved with a small change in performance with respect to the tree generated with centrally stored data. Experimental results for several well-known datasets are presented and compared with decision trees generated using Fisher's linear discriminant function with centrally stored data.