Three-objective genetics-based machine learning for linguistic rule extraction

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Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems (2020, methodology): “in [18] implemented a three-objective strategy to evolve fuzzy set of rules using a multi-objective genetic algorithm.”
Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning (2007, methodology): “Heuristic prescreening of candidate rules, however, is necessary in the candidate rule generation stage [15,16] when a GA-based rule selection method is to be applied to high-dimensional problems such as the wine data set with 13 attributes and the sonar data set with 60 attributes.”
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies (2008, background): “A step further is to include a third objective that minimizes the rule length (number of premises) [ 45 ], or the number of selected input variables [46].”
Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining (2004, methodology): “We iterated our MOGA much longer than [12] where the stopping condition was 1000 generations.”
Ten years of genetic fuzzy systems: current framework and new trends (2001, background): “Recent works have suggested both, accuracy and interpretability, as objectives of learning systems [73,76,79].”