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Synthetic minority oversampling technique in stages for unbalanced climate and rice dataset: the Office Du Niger case study

In this paper, we evaluate the impact of climate parameters on the yield of rice production at the Office du Niger (ON) in Mali using four classifiers. Three machine learning classification algorithms, namely k-Nearest Neighbor (KNN), Support Vector Classifier (SVC), and Logistic Regression (LR) and Multilayer Perceptron (MLP) classifier are utilized. Our experience reveals that the three classification algorithms of machine learning and Multilayer perceptron classifiers give poor results on the original dataset due to the problem of unbalanced data. However, oversampling techniques are applied to the dataset to improve these results. The technique we used is an oversampling technique step by step. Therefore, 100% SMOTE (Synthetic Minority Over-Sampling Technique) is applied to the minority class at each stage, i.e., the minority class is doubled at each step. We found out that the proposed technique gives better results for all these classification algorithms rather than applying SMOTE directly to balance the dataset and other balanced techniques such as class balance and resample. However, MLP gives better results compared to other machine learning classification algorithms using our technique.