A new local discriminative agglomeration for text classification

Fuzzy relational classifier(FRC) is a recently proposed nonlinear classifier, in which the unsupervised clustering is performed to explore the underlying structure of the data distribution, and to construct the subsequent classifier. Main advantage of FRC is the interpretable results of the classification prediction. However, the unsupervised FCM is highly sensitive to non-spherical data distribution and improper cluster numbers. In this paper, a new clustering method based on local discriminative information is proposed to group data based on both similarity and class labels, giving rise to that the constructed fuzzy relationship between the formed groups and the given classes is more reliable than unsupervised clustering method. During the classification period, neighbourhood information is incorporated into the classification mechanism to improve its performance. The experimental results on Routers-21578 text dataset and Fudan Chinese textset demonstrate that this new approach has overcome the above disadvantages of FRC and achieved prior robustness and classification performance in cost of small computational cost.