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Large volumes of data have been accumulated in identical or very similar contexts of decision making. To generate accurate and explanatory decision recommendations by using these data, this paper proposes a data-driven multi-criteria decision making (MCDM) method based on machine learning (ML) algorithms and the evidential reasoning (ER) approach. In the method, based on the assessments of all historical alternatives, a comparison framework is designed to determine the most appropriate ML algorithm with the highest predictive accuracy. An optimization model is then constructed to connect the appropriate ML algorithm with the ER approach. Through the optimization model, the difference between overall assessments derived from the ER approach and the predicted results derived from the appropriate ML algorithm is minimized to learn criterion weights. The learned criterion weights are used to generate accurate and explanatory decisions. Such a combination takes advantage of high predictability of ML algorithms and favorable interpretability of the ER approach simultaneously. To demonstrate the validity and applicability of the proposed method, it is used to aid the diagnosis of thyroid nodules for a tertiary hospital located in Hefei, Anhui, China. Its merits are further highlighted by its comparison with two traditional ER approaches and the appropriate ML algorithm. • Data-driven decision making based on ER approach and ML algorithms is proposed. • ML algorithms are compared based on their linkage with ER approach. • An optimization model is constructed to make explanatory decisions based on ML. • The proposed method is used to help radiologists diagnose thyroid nodules. • The proposed method is compared with decision making learning from accuracy and AUC. [ABSTRACT FROM AUTHOR]

• The following research questions are specifically addressed in this paper. • Which temporal window can capture the crucial phenological stages most effectively? • What spectral feature is most discriminative? • Whether the red edge bands of Sentinel-2 are important for accurate crop identification? Crop type mapping visualizes the spatial distribution patterns and proportions of the cultivated areas with different crop types, and is the basis for subsequent agricultural applications. Understanding the effectiveness of different temporal and spectral features in detailed crop classification can help users optimize temporal window selection and spectral feature space construction in crop type mapping applications. Therefore, in this study, we used time-series Sentinel-2 image data from Yi'an County, Heilongjiang province, China, to analyze the effectiveness of the temporal and spectral features used in three common machine learning classification methods: classification and regression tree (CART) decision tree, Support Vector Machine (SVM), and random forest (RF). For CART and SVM classifiers, the relative importance of the features was reflected by the order and frequency of attributes selected as the node and the square of the model weight. In RF, the change in prediction error as calculated by out of bag data is taken as the measure of feature importance. The standard deviation of the average value of all labeled pixels was used to evaluate the correctness of the unanimous conclusions drawn by these three methodologies. The quantitative evaluation results given by the confusion matrix show that random forest achieved the best overall accuracy, while support vector machine ranked second, and the decision tree algorithm yielded the least accurate classification results. From the perspective of feature importance, making full use of the discriminative information between different crops, and constructing a rational feature space, can help to improve classification accuracy significantly. In detail, the discriminative information between the different crop types is as follows: 1) images at the peak of the crop growth period are crucial in the classification of different crops; 2) the short-wave infrared bands are particularly suitable for fine crop classification; and 3) the red edge bands can effectively assist classification. Finally, our study achieved crop type mapping in the study area with an overall accuracy of 97.85% and a Kappa coefficient of 0.95. [ABSTRACT FROM AUTHOR]