Empirical studies on supervised learning have shown that ensembling methods lead to a model superior to the one built from a single learner under many circumstances [1], especially when learning from imperfect, such as biased or noise infected, information sources. In this paper, we provide a novel corrective classification (C2) design, which incorporates error detection, data cleansing and Bootstrap sampling to construct base learners that constitute the classifier ensemble. The essential goal is to reduce noise impacts and eventually enhance the learners built from noise corrupted data. We further analyze the importance of both the accuracy and diversity of base learners in ensembling, in order to shed some light on the mechanism under which C2 works. Experimental comparisons will demonstrate that C2 is not only superior to the learner built from the original noisy sources, but also more reliable than Bagging [2] or the Aggressive Classifier Ensemble (ACE) [3], which are two degenerate components/variants of C2.