Feature-subspace aggregating: ensembles for stable and unstable learners
- Authors: Ting, Kaiming , Wells, Jonathan , Tan, Swee , Teng, Shyh , Webb, Geoffrey
- Date: 2011
- Type: Text , Journal article
- Relation: Machine Learning Vol. 82, no. 3 (2011), p. 375-397
- Full Text: false
- Reviewed:
- Description: This paper introduces a new ensemble approach, Feature-Subspace Aggregating (Feating), which builds local models instead of global models. Feating is a generic ensemble approach that can enhance the predictive performance of both stable and unstable learners. In contrast, most existing ensemble approaches can improve the predictive performance of unstable learners only. Our analysis shows that the new approach reduces the execution time to generate a model in an ensemble through an increased level of localisation in Feating. Our empirical evaluation shows that Feating performs significantly better than Boosting, Random Subspace and Bagging in terms of predictive accuracy, when a stable learner SVM is used as the base learner. The speed up achieved by Feating makes feasible SVM ensembles that would otherwise be infeasible for large data sets. When SVM is the preferred base learner, we show that Feating SVM performs better than Boosting decision trees and Random Forests. We further demonstrate that Feating also substantially reduces the error of another stable learner, k-nearest neighbour, and an unstable learner, decision tree.
FaSS : Ensembles for stable learners
- Authors: Ting, Kaiming , Wells, Jonathan , Tan, Swee , Teng, Shyh , Webb, Geoffrey
- Date: 2009
- Type: Text , Conference paper
- Relation: 8th International Workshop on Multipul Classifier Systems (MCS 2009)
- Full Text: false
- Reviewed:
- Description: This paper introduces a new ensemble approach, Feature-Space Subdivision (FaSS), which builds local models instead of global models. FaSS is a generic ensemble approach that can use either stable or unstable models as its base models. In contrast, existing ensemble approaches which employ randomisation can only use unstable models. Our analysis shows that the new approach reduces the execution time to generate a model in an ensemble with an increased level of localisation in FaSS. Our empirical evaluation shows that FaSS performs significantly better than boosting in terms of predictive accuracy, when a stable learner SVM is used as the base learner. The speed up achieved by FaSS makes SVM ensembles a reality that would otherwise infeasible for large data sets, and FaSS SVM performs better than Boosting J48 and Random Forests when SVM is the preferred base learner