Fig 1.
The multilevel SVM framework consists of three phases: gradual training set coarsening, coarsest support vectors’ learning, and gradual support vectors’ refinement (uncoarsening).
Pairs of AkNN graphs correspond to two classes of learning.
Table 1.
Confusion matrix.
Table 2.
Public data sets.
Table 3.
Comparative G-mean results for ML(W)SVM against the regular SVM, WSVM, NB, C4.5, 5NN, and LR on academic datasets for different fractions of missing values (rmv) using the REM imputation method.
Table 4.
Computational time in seconds (not including the REM method).
Table 5.
The set “Example 1” has 10000 observations in each class. In set “Example 2”, the majority and minority classes contain 50400, and 33600 observations, respectively. For details about the data see [8].
Table 6.
Accuracy of financial risk problem with five risk classes (Example 1) using the REM imputation method.
Table 7.
Sensitivity, specificity and G-mean of financial risk problem with five risk classes (Example 1) using ML(W)SVM and REM imputation methods.
Table 8.
Comparison of Multilevel WSVM against Multilevel SVM and Adaptive Logistic Regression (LR) using the REM imputation method.
Improved results are in bold.