Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation

Table 3

Evaluation performance of different methods.

Methods		Accuracy	MAE	F1 score
ML (features required)	GNB	58.28% (±1.86%)	0.4422 (±0.0228)	47.56% (±2.78%)
	KNN	72.09% (±1.59%)	0.2891 (±0.0260)	62.60% (±4.42%)
	LLR	68.26% (±1.10%)	0.3212 (±0.0118)	46.31% (±2.72%)
	SVM	71.96% (±1.01%)	0.2874 (±0.0113)	52.22% (±4.14%)
	MLP	71.98% (±1.66%)	0.2885 (±0.0163)	59.46% (±8.19%)
	RF	69.49% (±0.93%)	0.3129 (±0.0008)	59.88% (±4.06%)
	AdaBoost	73.84% (±0.91%)	0.2705 (±0.0080)	64.16% (±9.64%)
DL (MTSI)	Shadow-CNN	61.63% (±1.05%)	0.4132 (±0.0129)	51.23% (±5.42%)
	SOCNN	76.06% (±1.04%)	0.2499 (±0.0080)	70.23% (±4.61%)