Using rule-based classifiers in systematic reviews: a semantic class association rules approach

Systematic review is the scientific process that provides reliable answers to a particular research question by interpreting the current pertinent literature. There is a significant shift from using manual human approach to decision support tools that provides a semi-automated screening phase by reducing the required time and effort to the group of experts. Most of proposed works apply supervised Machine Learning (ML) algorithms to infer exclusion and inclusion rules by observing a human screener. Unless, these techniques holds very little promise in study identification phase, because the rate of excluding citations erroneously still unreasonable. In this paper, we contribute to this line of works by proposing an alternative approach, not yet tested in this domain based on semantic rule-based classifiers. This approach involved applying a novel Hybrid Feature Selection Method (HFSM) within a Class Association Rules (CARs) algorithm. Experiments are conducted on a corpus resulting from an actual systematic review. The obtained results show that our algorithm outperforms the existing algorithms in the literature.