Abstract
Classification accuracy and execution time are two important parameters in the selection of classification algorithms. In our experiments, 12 different ensemble algorithms, and 11 single classifiers are compared according to their accuracies and train/test time over 36 datasets. The results show that Rotation Forest has the highest accuracy. However, when accuracy and execution time are considered together, Random Forest and Random Committees can be the best choices.
Keywords
Random forest
Computer science
Artificial intelligence
Ensemble learning
Selection (genetic algorithm)
Random subspace method
Execution time
Machine learning
Rotation (mathematics)
Pattern recognition (psychology)
Data mining
Support vector machine
Subject Areas
Data Mining Algorithms and Applications
·Information Systems ·Physical Sciences
Machine Learning and Data Classification
·Artificial Intelligence ·Physical Sciences
Face and Expression Recognition
·Computer Vision and Pattern Recognition ·Physical Sciences
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