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IDA
2007
Springer

Combining Bagging and Random Subspaces to Create Better Ensembles

9 years 3 months ago
Combining Bagging and Random Subspaces to Create Better Ensembles
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.
Pance Panov, Saso Dzeroski
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where IDA
Authors Pance Panov, Saso Dzeroski
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