TY - GEN
T1 - An ensemble learning technique for multipartite ranking
AU - Clémençon, Stéphan
AU - Robbiano, Sylvain
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Decision tree induction algorithms, possibly combined with a consensus technique, have been recently successfully extended to multipartite ranking. It is the goal of this paper to address certain aspects of their weakness, instability and lack of smoothness namely, by proposing dedicated ensemble learning strategies. A shown by numerical experiments, bootstrap aggregation combined with a certain amount of feature randomization dramatically improve performance of such ranking methods, in terms of accuracy and robustness both at the same time.
AB - Decision tree induction algorithms, possibly combined with a consensus technique, have been recently successfully extended to multipartite ranking. It is the goal of this paper to address certain aspects of their weakness, instability and lack of smoothness namely, by proposing dedicated ensemble learning strategies. A shown by numerical experiments, bootstrap aggregation combined with a certain amount of feature randomization dramatically improve performance of such ranking methods, in terms of accuracy and robustness both at the same time.
M3 - Conference contribution
AN - SCOPUS:84961789682
T3 - 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings
SP - 397
EP - 402
BT - 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings
PB - i6doc.com publication
T2 - 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015
Y2 - 22 April 2015 through 24 April 2015
ER -