Heterogeneous ensemble for feature drifts in data streams
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
| Hi-index | 0.00 |
One common approach to active learning is to iteratively train a single classifier by choosing data points based on its uncertainty, but it is nontrivial to design uncertainty measures unbiased by the choice of classifier. Query by committee suggests that given an ensemble of diverse but accurate classifiers, the most informative data points are those that cause maximal disagreement among the predictions of the ensemble members. However the method for finding ensembles appropriate to a given data set remains an open question. In this paper, the random subspace method is combined with active learning to create multiple instances of different classifier types, and an algorithm is introduced that adapts the ratio of different classifier types in the ensemble towards better overall accuracy. Here we show that the proposed algorithm outperforms C4.5 with uncertainty sampling, Naive Bayes with uncertainty sampling, bagging, boosting and the random subspace method with random sampling. To the best of our knowledge, our work is the first to adapt the ratio of classifiers in a heterogeneous ensemble for active learning.