Improving Bagging Ensembles for Class Imbalanced Data by Active Learning
[ 1 ] Instytut Informatyki, Wydział Informatyki, Politechnika Poznańska | [ P ] pracownik
2018
rozdział w monografii naukowej
angielski
- class imbalance
- active learning
- bagging ensembles
- under-sampling
EN Extensions of under-sampling bagging ensemble classifiers for class imbalanced data are considered. We propose a two phase approach, called Actively Balanced Bagging, which aims to improve recognition of minority and majority classes with respect to so far proposed extensions of bagging. Its key idea consists in additional improving of an under-sampling bagging classifier (learned in the first phase) by updating in the second phase the bootstrap samples with a limited number of examples selected according to an active learning strategy. The results of an experimental evaluation of Actively Balanced Bagging show that this approach improves predictions of the two different baseline variants of under-sampling bagging. The other experiments demonstrate the differentiated influence of four active selection strategies on the final results and the role of tuning main parameters of the ensemble.
17.11.2017
25 - 52
20