Using Error-Correcting Output Codes with Model-Refinement to Boost Centroid Text Classifier

In this work, we investigate the use of error-correcting output codes (ECOC) for boosting centroid text classifier. The implementation 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 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 can dramatically decrease the bias introduced by ECOC, and the combined classifier is comparable to or even better than SVM classifier in performance.