Figures & data
Figure 3. Structure of majority voting ensemble.
Figure 4. Proposed ensemble framework for classifier.
Figure 5. Performance comparison of classification accuracy in various schemes.
Figure 6. Performance comparison of time complexity involved in various classifiers.
Figure 1. Data flow diagram of BRNN algorithm.
Table 1. Types of kernel with its equations.
Figure 2. Ensemble framework.
Table 2. Dataset employed in the experiment.
Table 3. Description of attributes of pima Indian diabetes dataset.
Table 4. Description of attributes for CpG Island dataset.
Table 5. Description of attributes for wisconsin diagnostic breast cancer dataset.
Table 6. Confusion matrix.
Table 7. Confusion matrix for pima indian diabetes dataset.
Table 8. Confusion matrix for cpg island dataset.
Table 9. Confusion matrix for breast cancer dataset.
Table 10. Sensitivity and specificity for various classifiers using different datasets.
Table 11. Accuracies and time complexity using various classifiers on different dataset.