The propositionalization process tries to find distinctive features of the examples in a database to transform such relational data into a simpler representation. More informative features have a positive impact on the classification capabilities of the learning algorithms. In this work, we propose a new propositionalization method, which generates complex Boolean attributes using Grammar-Guided Genetic Programming (G3P). The generated attributes are compound formulas that combine word items coming from a Bag-of-Words (BoW) representation using Boolean operators. The proposal was assessed against three state-of-the-art simple-instance and multiple-instance propositionalization methods. The experimental results show that the proposed method achieves an improvement in terms of classification accuracy and a considerable reduction in the dimensionality of the resulting datasets.

命题化过程试图在数据库中找到示例的鲜明特征，以将这种关系数据转换为更简单的表示形式。更多信息功能对学习算法的分类能力有积极影响。在这项工作中，我们提出了一种新的命题化方法，该方法使用语法指导的遗传规划（G3P）生成复杂的布尔属性。生成的属性是复合公式，这些组合公式使用布尔运算符组合了来自单词袋（BoW）表示的单词项。根据三种最新的简单实例和多实例命题方法对提案进行了评估。