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- W2742671013 abstract "ABSTRACTThe ground truth data sets required to train supervised classifiers are usually collected as to maximize the number of samples under time, budget and accessibility constraints. Yet, the performance of machine learning classifiers is, among other factors, sensitive to the class proportions of the training set. In this letter, the joint effect of the number of calibration samples and the class proportions on the accuracy was systematically quantified using two state-of-the-art machine learning classifiers (random forests and support vector machines). The analysis was applied in the context of binary cropland classification and focused on two contrasted agricultural landscapes. Results showed that the classifiers were more sensitive to class proportions than to sample size, though sample size had to reach 2,000 pixels before its effect leveled off. Optimal accuracies were obtained when the training class proportions were close to those actually observed on the ground. Then, synthetic minority over-sa..." @default.
- W2742671013 created "2017-08-17" @default.
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- W2742671013 date "2017-08-06" @default.
- W2742671013 modified "2023-09-25" @default.
- W2742671013 title "The impact of training class proportions on binary cropland classification" @default.
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- W2742671013 doi "https://doi.org/10.1080/2150704x.2017.1362124" @default.
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