Association rule-based classification: A comprehensive review of methodologies and applications
Xiaojiao Geng, Zheng Yang, Lianmeng Jiao, Zhijie Zhou, Zongfang Ma
Abstract
As one of the most promising classification approaches , association rule-based classification (also called associative classification , AC) enables effectively integrating the classification tasks with association rule discovery techniques for deriving accurate, robust and interpretable results. The advantages of association rule discovery techniques over deep learning architectures in classification are mainly reflected by the aspects of better interpretability for users and higher accuracy for small sample data. Despite of great progress in both theoretical and applied aspects, there remains a lack of comprehensive and systematic overview for the recent development in AC. In light of this, this paper first conducts a statistical analysis of academic reports and related application achievements over the past decades, among which 317 are method-oriented and 200 are application-oriented. After that, through performing an in-depth analysis for these literatures, it then provides a comprehensive review for the overall learning framework, theoretical methodologies, application domains, as well as the key research challenges in the field of AC. Finally, this review displays some potential and meaningful research directions in the future by integrating the challenges with development trends, such as deep associative learning framework, human–machine intelligent associative system and semi-supervised learning within evidential framework, with the hope of assisting interested researchers gaining a quick understanding for the development trends in AC.