Table 15. Performance comparison with state-of-the-art approaches.

References	Target classes	Approach	Accuracy (%)
da Rocha Neto et al. (2011)	3	SVM	85.0
Karabulut & Ibrikci (2014)		LMT	89.0
Akben (2016)		MLP	83.0
Unal & Kocer (2013)		MLP	85.0
Alafeef et al. (2019)		ANN	98.5
Prasetio & Riana (2015)		KNN	93.0
Jiménez & Quintero-Ospina (2019)		Ensemble	90.0
Unal, Polat & Kocer (2014)		Fuzzy+SVM	96.4
Proposed		ETC with CR	99.0
Unal, Polat & Kocer (2016)	2	MSCBAW+RBDNN	99.3
Proposed		ETC with CR	99.5

Note:

Bold entries show the highest accuracy and indicate that the proposed approach outperforms state-of-the-art approaches.