2014/10/02 by Hugo Jair Escalante, Escalante, Hugo Jair, Mauricio A. García-Limón +9
Biochemistry, Genetics and Molecular Biology · Computer Science · #68T10 #68T50 #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Neural and Evolutionary Computing (cs.NE) #Text and Document Classification Technologies #cs.LG #cs.NE #msc:68T10 #msc:68T50
paper · pdf · doi:10.48550/arxiv.1410.0640
openalex publication_date 2014/10/02 · arxiv created 2014/10/06 · arxiv updated 2014/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.