Classifiers accuracy improvement based on missing data imputation

Mathematics

• Imputation 100%
• Classifier 95%
• Missing Data 88%
• Random Forest 49%
• Metric 44%
• Radar 43%
• Support Vector Machine 42%
• Missing Values 40%
• Neural Networks 31%
• Multi-class Classification 26%
• Wilcoxon Test 23%
• K-nearest Neighbor 22%
• Military 22%
• Effect Size 21%
• Multiple Imputation 21%
• Scanning 21%
• Statistical Significance 20%
• Receiver Operating Characteristic Curve 20%
• Discrete Data 20%
• Modulation 20%
• Nominal or categorical data 20%
• Performance 20%
• Repetition 19%
• Subset 18%
• Deletion 17%
• Class 10%
• Interval 9%
• Model 5%

Engineering & Materials Science

• Classifiers 59%
• Random forests 35%
• Radar 27%
• Support vector machines 27%
• Pulse modulation 24%
• Neural networks 20%
• Scanning 13%