Term-weighting learning via genetic programming for text classification

• Published in: Knowledge-based systems Vol. 83; pp. 176 - 189
• Main Authors: Escalante, Hugo Jair, García-Limón, Mauricio A., Morales-Reyes, Alicia, Graff, Mario, Montes-y-Gómez, Manuel, Morales, Eduardo F., Martínez-Carranza, José
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2015
• Subjects: Bag of words; Genetic programming; Representation learning; Term-weighting learning; Text mining; Text mining; Representation learning; Bag of words; Genetic programming; Term-weighting learning
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2015.03.025

•A new method for learning term-weighting schemes is proposed.•A genetic program searches for the scheme that maximizes classification performance.•The method is evaluated in text and image categorization, and authorship attribution. 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.