GU METRIC - A New Feature Selection Algorithm for Text Categorization

Gulden Uchyigit, Keith Clark


To improve scalability of text categorization and reduce over-fitting, it is desirable to reduce the number of words used for categorisiation. Further, it is desirable to achieve such a goal automatically without sacrificing the categorization accuracy. Such techniques are known as automatic feature selection methods. Typically this is done in the way that each word is assigned a weight (using a word scoring metric) and the top scoring words are then used to describe a document collection. There are several word scoring metrics which have been employed in literature. In this paper we present a novel feature selection method called the GU metric. The details of comparative evaluation of all the other methods are given. The results show that the GU metric outperforms some of the other well known feature selection methods.


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Paper Citation

in Harvard Style

Uchyigit G. and Clark K. (2007). GU METRIC - A New Feature Selection Algorithm for Text Categorization . In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-972-8865-89-4, pages 399-402. DOI: 10.5220/0002365503990402

in Bibtex Style

author={Gulden Uchyigit and Keith Clark},
title={GU METRIC - A New Feature Selection Algorithm for Text Categorization},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - GU METRIC - A New Feature Selection Algorithm for Text Categorization
SN - 978-972-8865-89-4
AU - Uchyigit G.
AU - Clark K.
PY - 2007
SP - 399
EP - 402
DO - 10.5220/0002365503990402