Table 2.

Optimization parameters.

	Parameter	Value
PSO	Number of clusters	5
	Number of iterations	100
	Number of population	50
	Inertia weight damping ratio	1
	Inertia value of weight	0.72979999
	Learning coefficient of personal	1.4961962
	Learning coefficient of global	1.4961962
O-MLM	Optimizer	Bayesian optimizer
	Max objective evaluations	20
	Min training set size	100
	Number of folds	5
	Prior probability	Emprical
AETL	Optimizer	Bayesian optimizer
	Learner type	Decision tree
	Optimization hyper parameters	Ensemble method
		Minimum leaf size
		Number of learning cycles
		Maximum number of splits
		Learning rate