Traditionally, fuzzy set-based approaches have performed excellently in modeling small to medium scale problem domains. This paper examines the scalability of fuzzy systems to a large-scale problem that is inherently vague and of text categorization. The paper presents two fuzzy probabilistic approaches to text classification and the corresponding machine learning algorithms to learn such systems from example data. The first approach follows the traditional fuzzy set paradigm, while the second approach fits within the modeling with words paradigm using granule features to represent the text problem domain.
['James G. Shanahan']
Modeling with words: an approach to text categorization