Mining m-grams by a granular computing approach for text classification

Text mining and text classification are gaining more and more importance in AI related research fields. Researchers are particularly focused on classification systems, based on structured data (such as sequences or graphs), facing the challenge of synthesizing interpretable models, exploiting gray-box approaches. In this paper, a novel gray-box text classifier is presented. Documents to be classified are split into their constituent words, or tokens. Groups of frequent m tokens (or m-grams) are suitably mined adopting the Granular Computing framework. By fastText algorithm, each token is encoded in a real-valued vector and a custom-based dissimilarity measure, grounded on the Edit family, is designed specifically to deal with m-grams. Through a clustering procedure the most representative m-grams, pertaining the corpus of documents, are extrapolated and arranged into a Symbolic Histogram representation. The latter allows embedding documents in a well-suited real-valued space in which a standard classifier, such as SVM, can safety operate. Along with the classification procedure, an Evolutionary Algorithm is in charge of performing features selection, which is able to select most relevant symbols – m-grams – for each class. This study shows how symbols can be fruitfully interpreted, allowing an interesting knowledge discovery procedure, in lights with the new requirements of modern explainable AI systems. The effectiveness of the proposed algorithm has been proved through a set of experiments on paper abstracts classification and SMS spam detection.