Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review

The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in Data Science successfully implemented various ACO methodologies rule-based classification. This family is referred to as AntMiner algorithms. Due flexibility framework, and availability alternative strategies at modular level, a systematic review on can benefit broader practitioners interested highly interpretable classification techniques. In this paper, we provided comprehensive each module Our motivation provide insight into current practices future research scope context discussions address methodologies, rule construction strategies, candidate selection metrics, quality evaluation functions, pruning methods continuous attributes, parameter selection, experimental settings. also reports summary real-life implementations classifiers diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, reliability engineering. These demonstrate potential application that be benefitted from methodological contributions technique.