Term-Weighting Learning via Genetic Programming for Text Classification
This addresses the challenge of manually designing term-weighting schemes for text classification, offering a method to automatically generate effective schemes, though it is incremental as it builds on existing genetic programming techniques.
The paper tackles the problem of learning term-weighting schemes for text classification by proposing a genetic programming approach that combines basic units to create discriminative schemes, resulting in TWSs that outperform traditional and recent methods across thematic, non-thematic, and image classification datasets.
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.