Constant depth circuits, Fourier transform, and learnability
Journal of the ACM (JACM)
Machine Learning
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
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
MobiMine: monitoring the stock market from a PDA
ACM SIGKDD Explorations Newsletter
A Principal Components Approach to Combining Regression Estimates
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
A new method for hierarchical clustering combination
Intelligent Data Analysis
Analyzing the techniques that improve fault tolerance of aggregation trees in sensor networks
Journal of Parallel and Distributed Computing
Selection-fusion approach for classification of datasets with missing values
Pattern Recognition
Identifying user preferences with Wrapper-based Decision Trees
Expert Systems with Applications: An International Journal
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This paper introduces orthogonal decision trees that offer an effective way to construct a redundancy-free, accurate, and meaningful representation of large decision-tree-ensembles often created by popular techniques such as Bagging, Boosting, Random Forests, and many distributed and data stream mining algorithms. Orthogonal decision trees are functionally orthogonal to each other and they correspond to the principal components of the underlying function space. This paper offers a technique to construct such trees based on the Fourier transformation of decision trees and eigen-analysis of the ensemble in the Fourier representation. It offers experimental results to document the performance of orthogonal trees on the grounds of accuracy and model complexity.