We address some crucial problem associated with text categorization, a local feature selection. It seems that intuitionistic fuzzy sets can be an effective and efficient tool making it possible to assess each term (from a feature set for each category) from a point of view of both its indicative and non-indicative ability. It is important especially for high dimensional problems to improve text filtering via a confident rejection of non-relevant documents. Moreover, we indicate that intuitionistic fuzzy sets are a good tool for the classification of imbalanced and overlapping classes, a commonly encountered case in text categorization. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Szmidt, E., & Kacprzyk, J. (2008). Using intuitionistic fuzzy sets in text categorization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5097 LNAI, pp. 351–362). https://doi.org/10.1007/978-3-540-69731-2_35
Mendeley helps you to discover research relevant for your work.