A Comparative Experimental Assessment of a Threshold Selection Algorithm in Hierarchical Text Categorization

Autor:	Eloisa Vargiu, Andrea Addis, Giuliano Armano
Rok vydání:	2011
Předmět:	Text categorization business.industry Computer science Artificial intelligence Data mining Machine learning computer.software_genre business Boosting methods for object categorization Selection algorithm Classifier (UML) computer
Zdroj:	Lecture Notes in Computer Science ISBN: 9783642201608 ECIR
DOI:	10.1007/978-3-642-20161-5_6
Popis:	Most of the research on text categorization has focused on mapping text documents to a set of categories among which structural relationships hold, i.e., on hierarchical text categorization. For solutions of a hierarchical problem that make use of an ensemble of classifiers, the behavior of each classifier typically depends on an acceptance threshold, which turns a degree of membership into a dichotomous decision. In principle, the problem of finding the best acceptance thresholds for a set of classifiers related with taxonomic relationships is a hard problem. Hence, devising effective ways for finding suboptimal solutions to this problem may have great importance. In this paper, we assess a greedy threshold selection algorithm aimed at finding a suboptimal combination of thresholds in a hierarchical text categorization setting. Comparative experiments, performed on Reuters, report the performance of the proposed threshold selection algorithm against a relaxed brute-force algorithm and against two state-of-the-art algorithms. Results highlight the effectiveness of the approach.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b0a21585b204696cf7f2920cf8aabbb2 https://doi.org/10.1007/978-3-642-20161-5_6 Zobrazit plný text záznamu