Table 9.

Top results (with threshold accuracy set at 80%) of numerical experiments for the Wavelet approach with hyperparameter optimization.

Model	Transformation	n_train	n_test	% for Training	Acc. [%]
Gradient Boosting	STD	22	53	70	96.23
Random Forest	STD	52	23	30	95.65
Gradient Boosting	STD	30	45	60	95.56
Gradient Boosting	MINMAX	37	38	50	94.74
Gradient Boosting	MINMAX	30	45	60	93.33
Gradient Boosting	NONE	30	45	60	93.33
XGB	MINMAX	22	53	70	92.45
Decision Tree	MINMAX	22	53	70	92.45
XGB	STD	22	53	70	92.45
XGB	NONE	22	53	70	92.45
Gradient Boosting	MINMAX	22	53	70	90.57
Gradient Boosting	NONE	22	53	70	90.57
LGBM	MINMAX	37	38	50	89.47
Gradient Boosting	STD	37	38	50	89.47
Decision Tree	STD	37	38	50	89.47
LGBM	STD	37	38	50	89.47
Gradient Boosting	NONE	37	38	50	89.47
LGBM	NONE	37	38	50	89.47
Decision Tree	NONE	30	45	60	88.89
Random Forest	NONE	52	23	30	86.96
Gradient Boosting	STD	60	15	20	86.67
Decision Tree	NONE	60	15	20	86.67
XGB	MINMAX	30	45	60	84.44
XGB	STD	30	45	60	84.44
Decision Tree	STD	30	45	60	84.44
XGB	NONE	30	45	60	84.44
LGBM	MINMAX	22	53	70	83.02
LGBM	STD	22	53	70	83.02
Decision Tree	NONE	22	53	70	83.02
LGBM	NONE	22	53	70	83.02
Random Forest	MINMAX	52	23	30	82.61
LGBM	MINMAX	30	45	60	82.22
LGBM	STD	30	45	60	82.22
LGBM	NONE	30	45	60	82.22
XGB	MINMAX	37	38	50	81.58
XGB	STD	37	38	50	81.58
XGB	NONE	37	38	50	81.58
Decision Tree	NONE	37	38	50	81.58
Random Forest	NONE	37	38	50	81.58
Random Forest	MINMAX	30	45	60	80.00
Random Forest	STD	30	45	60	80.00
LGBM	MINMAX	45	30	40	80.00
LGBM	STD	45	30	40	80.00
Gradient Boosting	NONE	45	30	40	80.00
LGBM	NONE	45	30	40	80.00
Random Forest	STD	60	15	20	80.00