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http://infotec.repositorioinstitucional.mx/jspui/handle/1027/212| Term-weighting learning via genetic programming for text classification | |
| Hugo Jair Escalante MAURICIO ALFONSO GARCIA LIMON Alicia Morales-Reyes Manuel Montes_y_Gómez Eduardo Morales JOSE MARTINEZ CARRANZA MARIO GRAFF GUERRERO | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Programación genética | |
| This paper describes a novel approach to learning term-weighting schemes (TWSs) in the context of text classification. In text mining a TWS determines the way in which documents will be represented in a vector space model, before applying a classifier. Whereas acceptable performance has been obtained with standard TWSs (e.g., Boolean and term-frequency schemes), the definition of TWSs has been traditionally an art. Further, it is still a difficult task to determine what is the best TWS for a particular problem and it is not clear yet, whether better schemes, than those currently available, can be generated by combining known TWS. We propose in this article a genetic program that aims at learning effective TWSs that can improve the performance of current schemes in text classification. The genetic program learns how to combine a set of basic units to give rise to discriminative TWSs. We report an extensive experimental study comprising data sets from thematic and non-thematic text classification as well as from image classification. Our study shows the validity of the proposed method; in fact, we show that TWSs learned with the genetic program outperform traditional schemes and other TWSs proposed in recent works. Further, we show that TWSs learned from a specific domain can be effectively used for other tasks. | |
| Knowledge-Based Systems 83 | |
| 2015 | |
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| Inglés | |
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| 00233_term-weighting_lear.pdf | 945.13 kB | Adobe PDF | Visualizar/Abrir |