Term-Weighting Learning via Genetic Programming for Text Classification

TL;DR

This paper introduces a genetic programming approach to automatically learn effective term-weighting schemes for text classification, outperforming traditional and recent methods across various datasets.

Contribution

It presents a novel genetic programming method to automatically generate discriminative term-weighting schemes, improving classification performance over existing schemes.

Findings

Learned TWSs outperform traditional schemes.

Domain-specific TWSs can be transferred effectively.

Genetic programming effectively combines basic units into discriminative TWSs.

Abstract

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.