Induction of decision trees as classification models through metaheuristics

•The three types of metaheuristic and the hyperheuristic-based methods to build decision tree induction algorithms are included, not just evolutionary-algorithm-based approaches for decision tree induction.•Three types of implementation strategies are introduced, according to the place where a metaheuristict is used in the decision tree induction process.•The differences in the solution representation are highlighted, as they impact the metaheuristic-based implementation.•The principal components of each method are detailed, such as fitness measures and variation operators.•An analysis of the experimental studies performed in these methods is conducted.