Ensemble learning based on approximate reducts and bootstrap sampling
Feng Jiang, Xu Yu, Junwei Du, Dunwei Gong, Youqiang Zhang, Yanjun Peng
Abstract
Ensemble learning is an effective approach for improving the generalization ability of base classifiers. To generate a set of accurate and diverse base classifiers, different data perturbation schemes have been proposed. For instance, Bagging perturbs the training data via bootstrap sampling. However, when a stable learning algorithm (e.g., KNN, Naive Bayes) is used to train base classifiers, the sole perturbation on the training data may not produce diverse base classifiers. In this paper, by using the attribute reduction technology in rough sets, a multi-modal perturbation-based algorithm (called 'E_EARBS') is proposed for the ensemble of base classifiers. E_EARBS simultaneously perturbs the feature space, training data and learning parameters, where the relative decision entropy(RDE)-based approximate reducts are used to perturb the feature space, and bootstrap sampling is used to perturb the training data. Experimental results show that E_EARBS can provide competitive solutions for ensemble learning.