Term-weighting learning via genetic programming for text classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Escalante:2015:KBS,

• author = "Hugo Jair Escalante and Mauricio A. Garcia-Limon and Alicia Morales-Reyes and Mario Graff and Manuel Montes-y-Gomez and Eduardo F. Morales and Jose Martinez-Carranza",
• title = "Term-weighting learning via genetic programming for text classification",
• journal = "Knowledge-Based Systems",
• year = "2015",
• volume = "83",
• pages = "176--189",
• keywords = "genetic algorithms, genetic programming, term-weighting learning, text mining, representation learning, bag of words",
• ISSN = "0950-7051",
• bibdate = "2015-05-11",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/kbs/kbs83.html#EscalanteGMGMMM15",
• URL = "http://dx.doi.org/10.1016/j.knosys.2015.03.025",
• DOI = "doi:10.1016/j.knosys.2015.03.025",
• URL = "http://www.sciencedirect.com/science/article/pii/S0950705115001197",
• abstract = "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 TWS (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 learnt with the genetic program outperform traditional schemes and other TWSs proposed in recent works. Further, we show that TWSs learnt from a specific domain can be effectively used for other tasks.",
• notes = "See http://arxiv.org/abs/1410.0640 \cite{journals/corr/EscalanteGMGMM14}",

}

Genetic Programming entries for Hugo Jair Escalante Mauricio Garcia-Limon Alicia Morales-Reyes Mario Graff Guerrero Manuel Montes-y-Gomez Eduardo F Morales Jose Martinez-Carranza

Citations