Automatic Textual Document Categorization Using Multiple Similarity-Based Models

We develop a similarity-based textual document categorization method called the generalized instance set (GIS) algorithm. GIS integrates the advantages of linear classifiers and k-nearest neighbour algorithm by generalization of selected instances. To further enhance the performance, we propose a meta-model framework which combines the strength of different variants of GIS algorithm as well as state-of-the-art existing algorithms using multivariate regression analysis on document feature characteristics. Document feature characteristics, derived from the training document set, capture some inherent properties of a particular category. Different from existing categorization methods, our proposed meta-model can automatically recommend a suitable algorithm for each category based on the category-specific statistical characteristics. In addition, our meta-model differs from existing multi-strategy learning in that our approach is not limited to the number and type of component classifiers. By flexible addition and substitution of different classifiers, incremental classification performance can be obtained. Extensive experiments have been conducted. The results confirm that our meta-model approach can exploit the advantage of its component algorithms, and demonstrate a better performance than existing algorithms. ∗Corresponding Author: Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong. {[email protected]} †Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong. {[email protected]}