An effective approach for improving the accuracy of a random forest classifier in the classification of Hyperion data

Random forest (RF) is one of the most powerful ensemble classifiers often used in machine learning applications. It has been found successful on many benchmarked data. However, the performance of an RF model is highly affected by the calibration of the model parameters. It requires optimization of t...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Applied geomatics 2020-03, Vol.12 (1), p.95-105
Hauptverfasser:	Chutia, Dibyajyoti, Borah, Naiwrita, Baruah, Diganta, Bhattacharyya, Dhruba Kumar, Raju, P. L. N., Sarma, K. K.
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Analysis Classification Earth Earth and Environmental Science Geographical Information Systems/Cartography Geography Geophysics/Geodesy Machine learning Measurement Science and Instrumentation Original Paper Parameters Principal components analysis Remote Sensing/Photogrammetry Surveying
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!