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EnglishThis paper describes Olex, a novel method for the automatic induction of rule-based text classifiers. Olex supports a hypothesis language of the form "if T1 or ⋯ or Tn occurs in document d, and none of Tn+1⋯Tn + m occurs in d, then classify d under category c, where each Ti is a conjunction of terms. The proposed method is simple and elegant. Despite this, the results of a systematic experimentation performed on the Reuters-21578, the Ohsumed, and the ODP data collections show that Olex provides classifiers that are accurate, compact, and comprehensible. A comparative analysis conducted against some of the most well-known learning algorithms (namely, Naive Bayes, Ripper, C4.5, SVM, and Linear Logistic Regression) demonstrates that it is more than competitive in terms of both predictive accuracy and efficiency.
Olex: effective rule learning for text categorization / Rullo, Pasquale; Iiritano, V. P. O. L. I. C. I. C. C. H. I. O. C. CUMBO AND S.. - In: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. - ISSN 1041-4347. - 21(2009), pp. 1118-1132.
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| Titolo: | Olex: effective rule learning for text categorization |
| Autori: | |
| Data di pubblicazione: | 2009 |
| Rivista: | |
| Citazione: | Olex: effective rule learning for text categorization / Rullo, Pasquale; Iiritano, V. P. O. L. I. C. I. C. C. H. I. O. C. CUMBO AND S.. - In: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. - ISSN 1041-4347. - 21(2009), pp. 1118-1132. |
| Handle: | http://hdl.handle.net/20.500.11770/158454 |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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