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What is RotBoost for Hyperspectral Classification about?

This paper discusses the challenges of hyperspectral data classification in machine learning, particularly due to high data dimensions. It evaluates the performance of the RotBoost method, which combines Rotation Forest and Adaboost, and finds that it achieves better accuracy than Rotation Forest, with a maximum accuracy of 88%. The study also examines the influence of base classifiers and boosting iterations on accuracy, concluding that these parameters have minimal impact on RotBoost

Author

Sasmita Dewi

Language

EN