Since the lackness of diversity may lead to bad performance in ensemble learning, a new two-phase ensemble learning method based on probability calibration was proposed, as well as two methods to reduce the impact of multiple collinearity. In the first phase, the probabilities given by the original classifiers were calibrated using different calibration methods. In the second phase, another classifier was trained using the calibrated probabilities and the final result was predicted. The different calibration methods used in the first phase provided diversity for the second phase, which has been shown to be an important factor to enhance ensemble learning. In order to address the limited improvement due to the correlation between base classifiers, two methods to reduce the multiple collinearity were also proposed, that is, choose-best and bootstrap sampling method. The choose-best method just selected the best base classifier among original and calibrated classifiers; the bootstrap method combined a set of classifiers, which were chosen from the base classifiers with replacement. The experimental results showed that the use of different calibrated probabilities indeed improved the effectiveness of the ensemble; after using the choose-best and bootstrap sampling methods, further improvement was also achieved. It means that probability calibration provides a new way to produce diversity, and the multiple collinearity caused by it can be solved by sampling method.