Binary classifiers versus AdaBoost for labeling of digital documents
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Title: | Binary classifiers versus AdaBoost for labeling of digital documents |
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Authors: | Montejo Ráez, Arturo | Ureña López, Luis Alfonso |
Keywords: | Clasificación automática de documentos | Comparación de algoritmos | Clasificación binaria | Benchmark | Automatic text categorization | Algorithms comparison | Binary classification |
Issue Date: | Sep-2006 |
Publisher: | Sociedad Española para el Procesamiento del Lenguaje Natural |
Citation: | MONTEJO RÁEZ, Arturo; UREÑA LÓPEZ, Luis Alfonso. "Binary classifiers versus AdaBoost for labeling of digital documents". Procesamiento del lenguaje natural. N. 37 (sept. 2006). ISSN 1135-5948, pp. 319-326 |
Abstract: | La asignación de términos de un vocabulario controlado (habitualmente un tesauro) a documentos en formato digital está abriendo la puerta a nuevas aplicaciones. En este artículo se comparan dos algoritmos avanzados para clasificación de documentos: la selección adaptativa de clasificadores base binarios y el algoritmo AdaBoost. Si bien ambos mostraron tiempos de respuesta similares, el primero proporcionó los mejores resultados sobre la partición hep-ex del corpus HEP, respaldando dicho método como una solución robusta al multi-etiquetado para grandes colecciones. | Assignment of labels from a controlled set of terms (usually a thesaurus) to digital version of documents is opening a wide range of new applications, now becoming powerful tools for digital libraries. In this paper we compare two different and advanced approaches for multi-label text categorization: the adaptive selection of binary base classifiers and the AdaBoost algorithm. Though both of them showed similar response times on producing final labels, the use of adaptive selection of binary classifiers performed better than AdaBoost on the hep-ex partition of the HEP corpus, confirming this method as a robust solution for multi-label of large collections. |
Sponsor: | This work has been partially supported by the Spanish Government under project R2D2-RIM (TIC2003-07158-C04-04). |
URI: | http://hdl.handle.net/10045/3341 |
ISSN: | 1135-5948 |
Language: | eng |
Type: | info:eu-repo/semantics/article |
Appears in Collections: | Procesamiento del Lenguaje Natural - Nº 37 (septiembre 2006) |
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