EVALUATION OF ROBUSTNESS OF ENSEMBLE LEARNERS TO NOISY DATA

Abstract

Discovering noisy data and classification of noisy data sets are problematic issues associated with noisy data sets. In our work, we used 36 UCI data sets that consist of differeent rates of noisy data to measure robustness of five ensemble learners and two basic classifiers to noisy data. According to classification success ratesof our study, Random Subspace and Bagging are more robust to noisy data than other ensemble learners and simple classifiers.