Classify test datasets with best trained classifiers

[elboyran]

Dataset 1 (417px=250m)

• [x] Retrain and save model 1.21 Ensemble/ Subspace KNN
• [x] Classify test data
• [x] Generate and save confusion matrix
• [ ] Generate and save performance measures

Dataset 2 (333px=200m)

• [ ] Retrain and save model 1.12 KNN/ Fine KNN
• [ ] Classify test data
• [ ] Generate and save confusion matrix
• [ ] Generate and save performance measures

Dataset 3 (250px=150m)

• [x] Retrain and save model 1.21 1.12 KNN/ Fine KNN
• [x] Classify test data
• [x] Generate and save confusion matrix
• [ ] Generate and save performance measures

Dataset 4 (167px=100m)

• [ ] Retrain and save model 1.12 KNN/ Fine KNN
• [ ] Classify test data
• [ ] Generate and save confusion matrix
• [ ] Generate and save performance measures

Dataset 6 (100px=80m)

• [ ] Retrain and save model Weighted KNN
• [ ] Classify test data
• [ ] Generate and save confusion matrix
• [ ] Generate and save performance measures

For all datasets:

• [ ] Produce comparison performance plots and save as fig and PDF
• [ ] Share results by email and BeeHub

[elboyran]

The trained models, confusion matrices and performance measures are saved as MAT files at C:\Projects\DynaSlum\Results\Classification3Classes\Classifiers

The generated model codes are saved at C:\Projects\DynaSlum\Software\SateliteImaging\MATLAB\CLassifierTraining\code

[elboyran]

For Dataset 1, all test data have been classified as 'NonBuildUp'! 👎 Results for Dataset 3 are also not good- no test Slums were classified as Slums! 👎