Chinese text categorization study based on CBM learning

Text Categorization (TC) is an important component in many information organization and information management tasks. In many TC applications, the case-base grows at a fast rate and this causes inefficiency in the case retrieval process. Using Case-Base Maintenance learning via the GC (Generalization Capability) algorithm, which can reduce the case number into KNN algorithm, can improve efficiency when indexing near neighbor in K-Nearest Neighbor algorithm. The numerical experiments prove the validity of this learning algorithm. Since K-NN algorithm is used extensively to a variety of areas, we can improve classification performance further in TC.