Fig 1.
An illustration of proposed scheme.
Table 1.
Summary of EEE studies.
Table 2.
Metaheuristic optimization work.
Fig 2.
Proposed MoMdbWE framework.
Fig 3.
Tier 1-Multi-dimensional Bagging (Mdb) scheme.
Table 3.
Implementation settings of Base algorithms.
Table 4.
Parameters initialization for optimization algorithms.
Fig 4.
Initial SP of SVR (using epsilon and coef0).
Fig 5.
Sub_SP of SVR (using epsilon and coef).
Table 5.
Sub_SPs ranges and optimized values of SVR hyperparameters.
Fig 6.
Initial SP of SVR (using gamma and coef0).
Fig 7.
Sub_SP of SVR (using gamma and coef0).
Fig 8.
Initial SP of SVR (using gamma and cost).
Fig 9.
Sub_SP of SVR(using gamma and cost).
Fig 10.
Initial SP of SVR (using coef0 and cost).
Fig 11.
Sub_SPs of SVR (using coef0 and co.
Fig 12.
Mdb and MoWE performance on Albrecht dataset.
Fig 13.
Mdb and MoWE performance on Albrecht dataset.
Fig 14.
Mdb and MoWE performance on Albrecht dataset.
Fig 15.
Mdb and MoWE performance on Albrecht dataset.
Table 6.
Initial SPs of three base algorithm.
Table 7.
Descriptive and statistical details of effort estimation datasets used.
Fig 16.
Mdb and MoWE performance on Albrecht dataset.
Fig 17.
Mdb and MoWE performance on Albrecht dataset.
Fig 18.
Mdb and MoWE performance on Albrecht dataset.
Fig 19.
Mdb and MoWE performance on Albrecht dataset.
Fig 20.
Mdb and MoWE performance on Cocomo81 dataset.
Fig 21.
Mdb and MoWE performance on Finnish dataset.
Fig 22.
Mdb and MoWE performance on Kitchenham dataset.
Fig 23.
Mdb and MoWE performance on Maxwell dataset.
Table 8.
SA and effect size evaluation of Proposed Mdb and MoWE.
Table 9.
Wilcoxon test results comparing Mdb schemes with solo learners and normal bagging algorithm.
Table 10.
Wilcoxon test results comparing MoWE with other models.
Table 11.
Comparative evaluation of past EEE work with proposed MoMdbWE scheme.