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C4.5: programs for machine learning
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Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Exact bootstrap k-nearest neighbor learners
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Ensembles of probability estimation trees for customer churn prediction
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Combining bagging, boosting, rotation forest and random subspace methods
Artificial Intelligence Review
ETL ensembles for chunking, NER and SRL
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Random forests are one of the best performing methods for constructing ensembles. They derive their strength from two aspects: using random subsamples of the training data (as in bagging) and randomizing the algorithm for learning base-level classifiers (decision trees). The base-level algorithm randomly selects a subset of the features at each step of tree construction and chooses the best among these. We propose to use a combination of concepts used in bagging and random subspaces to achieve a similar effect. The latter randomly select a subset of the features at the start and use a deterministic version of the base-level algorithm (and is thus somewhat similar to the randomized version of the algorithm). The results of our experiments show that the proposed approach has a comparable performance to that of random forests, with the added advantage of being applicable to any base-level algorithm without the need to randomize the latter.