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Table 1.
Comparing the classification performance of the global LS-SVM model to the GLocal-LS-SVM models (40-100 data-Partitions).
Table 2.
Comparing the classification performance of the standard SVM model to the Glocal-SVM models (40-100 data-Partitions).
Table 3.
Comparing the average time performance, in seconds, of the global LS-SVM model to the GLocal-LS-SVM models (100-40 Partitions), in addition to comparing the number of the average data points used to train the general model.
Table 4.
Comparing the time performance, in seconds, of the standard SVM model to the Glocal-SVM models (40-100 Partitions).
Table 5.
Comparing the average error performance of the GLocal LS-SVM, and LS-SVM applied to the breast cancer Wisconsin (diagnostic) dataset.
Table 6.
Comparing the average time performance, in seconds, of the GLocal-LS-SVM model to the global LS-SVM model, Glocal-SVM, and standard SVM applied to the breast cancer Wisconsin (diagnostic) dataset.
Table 7.
Comparing the average error performance of the GLocal-LS-SVM and LS-SVM applied to the Pima Indians Diabetes dataset.
Table 8.
Comparing the average time performance, in seconds, of the GLocal-LS-SVM model to the global LS-SVM model, Glocal-SVM, and standard SVM applied to the Pima Indians diabetes dataset.
Table 9.
Comparing the average error performance of the GLocal LS-SVM, LS-SVM, Ravi (2017), kNN-SVM, and kNN-LS-SVM models applied to the Daphnet FoG dataset.
Table 10.
Comparing the average time performance, in seconds, of the GLocal-LS-SVM model to the global LS-SVM model, Glocal-SVM, and standard SVM applied to the Daphnet FoG dataset.