Abstract
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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Lertnattee, V., Theeramunkong, T. (2003). Term-length Normalization for Centroid-based Text Categorization. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_113
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DOI: https://doi.org/10.1007/978-3-540-45224-9_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40803-1
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