Table 1.
Comparison of existing frameworks integrating SVR with other machine learning models for missing value imputation in non-class attributes.
Table 2.
Key contributions of the proposed SGA-DT framework in addressing identified research gaps.
Fig 1.
Proposed SGA-DT framework.
Table 3.
SVR hyperparameters and genetic algorithm configuration.
Table 4.
Characteristics of datasets.
Table 5.
Performance results of various integrated frameworks across all the datasets.
Fig 2.
Performance comparison of SGA-DT with other integrated frameworks in terms of accuracy.
Fig 3.
Performance comparison of SGA-DT with other integrated frameworks in terms of precision.
Fig 4.
Performance comparison of SGA-DT with other integrated frameworks in terms of recall.
Fig 5.
Performance comparison of SGA-DT with other integrated frameworks in terms of F-measure.
Fig 6.
Boxplot analysis of performance metrics for SGA-DT and other integrated frameworks.
Fig 7.
Effect of percentage of missing value on various performance parameters.
Fig 8.
Pruned decision tree generated for Breast Cancer dataset (ccp_alpha = 0.003).
Fig 9.
Pruned decision tree generated for Mammographic dataset (ccp_alpha = 0.003).
Fig 10.
Pruned decision tree generated for Hepatitis dataset (ccp_alpha = 0.007).