Title

Term-weighting learning via genetic programming for text classification

Author

Hugo Jair Escalante

MAURICIO ALFONSO GARCIA LIMON

Alicia Morales-Reyes

Manuel Montes_y_Gómez

Eduardo Morales

JOSE MARTINEZ CARRANZA

MARIO GRAFF GUERRERO

Access level

Open Access

Summary or description

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.

Publisher

Knowledge-Based Systems 83

Publish date

2015

Publication type

Article

Format

application/pdf

Language

English

Audience

Researchers

Source repository

Repositorio Institucional de INFOTEC

Downloads

824

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