A hybrid feature selection rule measure and its application to systematic review

Abstract

Systematic review is the scientific process that provides reliable answers to a particular research question. 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. Text classification is useful in determining the statistical significance level of association rules to reduce workload in the systematic review. Several approaches to generate a Rule set for rule based classifiers were proposed in the literature. In this paper, we show that statistic as well as semantic measures of a rule can be combined and effectively computed as a hybrid feature selection rule measure (HFSRM). Moreover, we propose a new algorithm called Rules7-hybrid feature selection (Rules7-HFSRM) by combining the classical algorithm Rules7 and the HFSRM and then used it on the systematic review problem. Our results show that our algorithm significantly outperforms the state-of-the-art benchmark algorithms in the systematic review context.