A Comparative Study of Thresholding Strategies in Progressive Filtering
| Autor: | Eloisa Vargiu, Andrea Addis, Giuliano Armano |
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| Rok vydání: | 2011 |
| Předmět: | |
| Zdroj: | AI*IA 2011: Artificial Intelligence Around Man and Beyond ISBN: 9783642239533 AI*IA |
| DOI: | 10.1007/978-3-642-23954-0_4 |
| Popis: | Thresholding strategies in automated text categorization are an underexplored area of research. Indeed, thresholding strategies are often considered a post-processing step of minor importance, the underlying assumptions being that they do not make a difference in the performance of a classifier and that finding the optimal thresholding strategy for any given classifier is trivial. Neither these assumptions are true. In this paper, we concentrate on progressive filtering, a hierarchical text categorization technique that relies on a local-classifier-per-node approach, thus mimicking the underlying taxonomy of categories. The focus of the paper is on assessing TSA, a greedy threshold selection algorithm, against a relaxed brute-force algorithm and the most relevant state-of-the-art algorithms. Experiments, performed on Reuters, confirm the validity of TSA. |
| Databáze: | OpenAIRE |
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