The module enables learning with only a small initial labeled data set via active learning. This semi-supervised learning algorithm interactively query the user to label the regions that are most useful to improve the overall classification score. With this technique, the number of examples to learn the classification is often much lower than the number of examples needed in normal supervised algorithms. You should start the classification with a small training set and run the module multiple times to label new informative samples to improve the classification score. The score metric is the number of correctly predicted labels over the total number of samples in the test set.
The samples that are chosen to be labeled are the ones where the class prediction is the most uncertain . Moreover, from the more uncertain samples, only the most different samples are kept . This diversity heuristic takes into account for each uncertain sample the distance to its closest neighbour and the average distance to all other samples. This ensures that newly labeled samples are not redundant with each other.
The learning data should be composed of features extracted from the regions, for example with the i.segment.stats module. The features of the training set, the test set and the unlabeled set should be in three different files in csv format. The first line of each file must be a header containing the features' name. Every regions should be uniquely identified by the first attribute. The classes for the training and test examples should be the second attribute.
Example of a training and test files :
cat,Class_num,attr1,attr2,attr3 167485,4,3.546,456.76,6.76 183234,6,5.76,1285.54,9.45 173457,2,5.65,468.76,6.78
Example of an unlabeled file :
cat,attr1,attr2,attr3 167485,3.546,456.76,6.76 183234,5.76,1285.54,9.45 173457,5.65,468.76,6.78
The training set can be easily updated once you have labeled new samples. Create a file to specify what label you give to which sample. This file in csv format should have a header and two attributes per line : the ID of the sample you have labeled and the label itself. The module will transfer the newly labeled samples from the unlabeled set to the training set, adding the class you have provided. This is done internally and does not modify your original files.
If the user wants to save the changes in new files according to the updates, new files can be created with the new labeled samples added to the training file and removed from the unlabeled file. Just specify the path of those output files in the parameters (training_updated, unlabeled_updated).
Example of an update file :
cat,Class_num 194762,2 153659,6 178350,2
Here are more details on a few parameters :
r.object.activelearning training_set=/path/to/training_set.csv \ test_set=/path/to/test_set.csv \ unlabeled_set=/path/to/unlabeled_set.csv Parameters used : C=146.398423284, gamma=0.0645567086567, lambda=0.25 12527959 9892568 13731120 15445003 13767630 Class predictions written to predictions.csv Training set : 70 Test set : 585 Unlabeled set : 792 Score : 0.321367521368
r.object.activelearning training_set=/path/to/training_set.csv \ test_set=/path/to/test_set.csv \ unlabeled_set=/path/to/unlabeled_set.csv \ update=/path/to/update.csv Parameters used : C=101.580687073, gamma=0.00075388337475, lambda=0.25 Class predictions written to predictions.csv Training set : 75 Test set : 585 Unlabeled set : 787 Score : 0.454700854701 8691475 9321017 14254774 14954255 15838185
The memory usage for ~1450 samples of 52 features each is around ~650 kb. This number can vary due to the unpredictablity of the garbage collector's behaviour. Everything is computed in memory; therefore the size of the data is limited by the amount of RAM available.
Available at: r.object.activelearning source code (history)
Latest change: Monday Jan 30 19:52:26 2023 in commit: cac8d9d848299297977d1315b7e90cc3f7698730
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