With the aim of improving the performance of centroid text classifier, we attempt to make use of the advantages of Error-Correcting Output Codes (ECOC) strategy. The framework is to decompose one multi-class problem into multiple binary problems and then learn the individual binary classification problems by centroid classifier. However, this kind of decomposition incurs considerable bias for centroid classifier, which results in noticeable degradation of performance for centroid classifier. In order to address this issue, we use Model-Refinement strategy to adjust this so-called bias. The basic idea is to take advantage of misclassified examples in the training data to iteratively refine and adjust the centroids of text data. The experimental results reveal that Model-Refinement strategy can dramatically decrease the bias introduced by ECOC, and the combined classifier is comparable to or even better than SVM classifier in performance.