  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.
