A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting
S Hoche, S Wrobel
Boosting has established itself as a successful technique for decreasing the generalization error of classification learners by basing predictions on ensembles of hypotheses. While previous research has shown that this technique can be made to work efficiently even in the context of multirelational learning by using simple learners and active feature selection, such approaches have relied on simple and static methods of determining feature selection ordering a priori and adding features only in a forward manner. In this paper, we investigate whether the distributional information present in boosting can usefully be exploited in the course of learning to reweight features and in fact even to dynamically adapt the feature set by adding the currently most relevant features and removing those that are no longer needed. Preliminary results show that these more informed feature set evolution strategies surprisingly have mixed effects on the number of features ultimately used in the ensemble, and on the resulting classification accuracy.
Conference Proceedings/Title of Journal: Proceedings of the 13th International Conference on Inductive Logic Programming, LNCS 2835
http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=2000291
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Hoche, S., & Wrobel, S. (2003). A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting. In Unknown (pp. 180 - 196). Springer. http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=2000291
Hoche, S ; Wrobel, S. / A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting. Unknown. Springer, 2003. pp. 180 - 196
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title = "A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting",
abstract = "Boosting has established itself as a successful technique for decreasing the generalization error of classification learners by basing predictions on ensembles of hypotheses. While previous research has shown that this technique can be made to work efficiently even in the context of multirelational learning by using simple learners and active feature selection, such approaches have relied on simple and static methods of determining feature selection ordering a priori and adding features only in a forward manner. In this paper, we investigate whether the distributional information present in boosting can usefully be exploited in the course of learning to reweight features and in fact even to dynamically adapt the feature set by adding the currently most relevant features and removing those that are no longer needed. Preliminary results show that these more informed feature set evolution strategies surprisingly have mixed effects on the number of features ultimately used in the ensemble, and on the resulting classification accuracy. ",
author = "S Hoche and S Wrobel",
note = "Conference Proceedings/Title of Journal: Proceedings of the 13th International Conference on Inductive Logic Programming, LNCS 2835",
booktitle = "Unknown",
Hoche, S & Wrobel, S 2003, A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting. in Unknown. Springer, pp. 180 - 196. <http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=2000291>
A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting. / Hoche, S; Wrobel, S.
Unknown. Springer, 2003. p. 180 - 196.
T1 - A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting
AU - Hoche, S
AU - Wrobel, S
N1 - Conference Proceedings/Title of Journal: Proceedings of the 13th International Conference on Inductive Logic Programming, LNCS 2835
N2 - Boosting has established itself as a successful technique for decreasing the generalization error of classification learners by basing predictions on ensembles of hypotheses. While previous research has shown that this technique can be made to work efficiently even in the context of multirelational learning by using simple learners and active feature selection, such approaches have relied on simple and static methods of determining feature selection ordering a priori and adding features only in a forward manner. In this paper, we investigate whether the distributional information present in boosting can usefully be exploited in the course of learning to reweight features and in fact even to dynamically adapt the feature set by adding the currently most relevant features and removing those that are no longer needed. Preliminary results show that these more informed feature set evolution strategies surprisingly have mixed effects on the number of features ultimately used in the ensemble, and on the resulting classification accuracy.
AB - Boosting has established itself as a successful technique for decreasing the generalization error of classification learners by basing predictions on ensembles of hypotheses. While previous research has shown that this technique can be made to work efficiently even in the context of multirelational learning by using simple learners and active feature selection, such approaches have relied on simple and static methods of determining feature selection ordering a priori and adding features only in a forward manner. In this paper, we investigate whether the distributional information present in boosting can usefully be exploited in the course of learning to reweight features and in fact even to dynamically adapt the feature set by adding the currently most relevant features and removing those that are no longer needed. Preliminary results show that these more informed feature set evolution strategies surprisingly have mixed effects on the number of features ultimately used in the ensemble, and on the resulting classification accuracy.
BT - Unknown
PB - Springer
Hoche S, Wrobel S. A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting. In Unknown. Springer. 2003. p. 180 - 196