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Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Using Boosting in a Multilabel Text Categorization Problem

Using Boosting in a Multilabel Text Categorization Problem
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Author(s): Jorge Muruzabal (University Rey Juan Carlos, Spain) and Eduardo G. Souto (University Rey Juan Carlos, Spain)
Copyright: 2003
Pages: 3
Source title: Information Technology & Organizations: Trends, Issues, Challenges & Solutions
Source Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59140-066-0.ch105
ISBN13: 9781616921248
EISBN13: 9781466665330

Abstract

The issue of learning to predict the various specific subjects or categories to which individual pieces of text belong is central in automatic classification. The Boosting scheme has been extensively tested and show an interesting record of success. In this paper we examine its performance in a well-known database of Medline research documents known as cystic fibrosis. This database is highly versatile for systematic experimentation. We empirically demonstrate this approach, discuss its overall usefulness and scope, and provide a detailed roadmap of further research in the area.

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