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K. Fuka and R. Hanka, “Feature Set Reduction for Document Classification Problems,” IJCAI-01 Workshop: Text Learning: Beyond Supervision, Seattle, August 2001, 2001.

has been cited by the following article:

• TITLE: The Role of Rare Terms in Enhancing the Performance of Polynomial Networks Based Text Categorization

  AUTHORS: Mayy M. Al-Tahrawi

  KEYWORDS: Polynomial Networks; Text Categorization; Document Classification; Infrequent Terms; Rare Terms

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.5 No.2, May 21, 2013

  ABSTRACT: In this paper, the role of rare or infrequent terms in enhancing the accuracy of English Text Categorization using Polynomial Networks (PNs) is investigated. To study the impact of rare terms in enhancing the accuracy of PNs-based text categorization, different term reduction criteria as well as different term weighting schemes were experimented on the Reuters Corpus using PNs. Each term weighting scheme on each reduced term set was tested once keeping the rare terms and another time removing them. All the experiments conducted in this research show that keeping rare terms substantially improves the performance of Polynomial Networks in Text Categorization, regardless of the term reduction method, the number of terms used in classification, or the term weighting scheme adopted.