Conference Paper

Term-length Normalization for Centroid-based Text Categorization

DOI: 10.1007/978-3-540-45224-9_113 Conference: Knowledge-Based Intelligent Information and Engineering Systems, 7th International Conference, KES 2003, Oxford, UK, September 3-5, 2003, Proceedings, Part I
Source: DBLP


Centroid-based categorization is one of the most popular algorithms in text classification. Normalization is an important
factor to improve performance of a centroid-based classifier when documents in text collection have quite different sizes.
In the past, normalization involved with only document- or class-length normalization. In this paper, we propose a new type
of normalization called term-length normalization which considers term distribution in a class. The performance of this normalization
is investigated in three environments of a standard centroid-based classifier (TFIDF): (1) without class-length normalization,
(2) with cosine class-length normalization and (3) with summing weight normalization. The results suggest that our term-length
normalization is useful for improving classification accuracy in all cases.

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