This paper collects an annual e-waybills data from a public freight platform database in one province in China for predicting the type of goods that are transported between different cities with a particular tim
These algorithms range from random forests classifiers [38] to neural network formulations [39] to deep learning architectures [40]. The training datasets of these bot detection algorithms consists of a wide variety of bot types from different countries. These algorithms are constructed as generic ...
Thus, we can conclude that when comparing the performance of classifiers and using different input configurations, the choice of the classifier significantly affects the results. Specifically, to gain further insights about the groups that statistically differ, we performed the Nemenyi test. Results of...
To enhance state-of-the-art, the pros of standalone machine learning classifiers and neural networks have been utilized. The stacked model obtained 100% accuracy on the testing data when using the decision tree classifier as the meta model. This study has been cross validated five-fold and ...
The sparse SVM model led to classification results of 73% sensitivity and 75% specificity [16]. An ensemble classifier consisting of SVM, random forest (RF), and Fisher's linear discri- minant analysis (FLDA) classifiers resulted in a sensitivity of 87%, a specificity of 90%, area under ...
Classifiers were trained on lung cancer, pancreatic cancer, HCC and healthy samples. The same random seed (seed = 5) was used in every random forest analysis for consistency. The top 15 features shared by at least 30 LOO iterations with the high- est mean decrease Gini with the highest ...
Six Rough/Fuzzy-Rough classifiers, coupled with Random Forest and RIPPER classifiers, were trained on the patient data set via a 10-fold stratified cross-validation process. The rule-based rough-set learning model yielded the highest overall accuracy, sensitivity, and specificity, exceeding 99%. ...
SVM: Support Vector Machine; Sn: Sensitivity; Sp: Specificity; Acc: Accuracy; MCC: Matthews Correlation Coefficient Serotype Feature selection Classifiers Bin size Number of features Sn Sp Acc MCC Ia OneR Random Forest 9 42 95.1% 89.1% 90.9% 0.804 SVM 9 38 93.5% 91.9% 92.4% 0.828 Ib ...
(2019) used Sentinel-1 SAR data with decision tree and random forest classifiers to map paddy rice with an accuracy above 95%. Li et al. (2020) developed an operational automatic approach for rice mapping with an accuracy above 89% using threshold backscatter values of water and rice crop ...
1. A data mining platform for generating an output comprising knowledge discovered from analysis of a plurality of data sets comprising heterogeneous data types or data from heterogeneous data sources, wherein the data points within the data sets comprise a plurality of descriptive features of varied...