Formula : To measure the density score at the next iteration k+1 of the Active Learning active learning loop ? we need to update it based on the new labels: Lk from past iteration k for each data point xj within a radius of rb from each labeled data point xy.
Once the density rank is update ? we can retrain the model and move to the next Active Learning iteration of the active learning loop. In the next iteration ? we explore new dense areas of the feature space thanks to the updated rank ? and we show new samples to the human-in-the-loop in exchange of labels (Figure 2 below).
Figure : Active Learning Iteration k: the user labels where the density score is highest ? then the density score is locally reduce where new labels were assigned. k + 1: the user labels now in another dense area of the feature space since the density score was reduced in previously explored areas. Conceptually ? the yellow cross stands for where new labels are assigned and the red one where the density has been reduced.
Wrapping Up
In this episode ? we’ve looked at
Label density as an active list to data sampling strategy
Labeling in all dense areas of feature space
Measuring the density of features space with the Euclidean distance measure and the weighted mean subtractive clustering approach
In the next blog article in this series ? we’ll be looking why social media for pharmacies at model uncertainty. This is an active sampling technique based on the prediction probabilities of the model on still unlabeled rows. Coming soon!
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