Gaussian Radial Basis Function Neural Network with Correlation Based Feature Selection Applied to Medical Text Categorization

Text categorization is an important field for information processing systems. Particularly, medical text processing is a popular research area that makes use of classification algorithms and dimension reduction strategies from machine learning field. In this study, we propose a three stage algorithm to automatically categorize medical text from OHSUMED corpus. In the proposed algorithm, we use Correlation Based Feature Filtering on top of Radial Basis Function Neural Network. The algorithm for 12 sample datasets produces 0.890 in terms macro average F-measure. In this context, both Correlation based Feature Filtering as a feature elimination strategy and Radial Basis Function Neural Network as text categorization algorithm are promising methods