Table 1.
Comparison of literature content.
Fig 1.
Diagonally dominant MGA and initial MGA comparison chart.
Fig 2.
MGA algorithm flowchart.
Fig 3.
KNN algorithm flowchart.
Fig 4.
The impact of different k-value classifications.
Fig 5.
GA-KNN algorithm flowchart.
Fig 6.
Run chart of multi feature construction method.
Fig 7.
Run chart of multi feature construction method.
Table 2.
Parametric environment.
Fig 8.
Convergence results of different dimensional functions under different algorithms.
Fig 9.
Change curve of classifier recognition rate and k-value.
Table 3.
The number of features and classifier recognition rate in different weights.
Table 4.
Data set and experimental parameters.
Table 5.
Sensitivity analysis of the proposed algorithm.
Fig 10.
Average accuracy of removing redundant content.
Fig 11.
Efficiency analysis of classifier performance improvement in three datasets.
Table 6.
Dataset and experimental parameters.
Table 7.
Feature selection ablation experiment of optimized genetic algorithm in high-dimensional data processing.
Fig 12.
NMI of several genetic optimization algorithms on different datasets.
Table 8.
Chi-square test analysis.