RNN based Batch Mode Active Learning Framework
Gaurav Maheshwari, Vikram Pudi · The European Symposium on Artificial Neural Networks · 2012
Active Learning has been applied in many real world classification tasks to reduce the amount of labeled data required for training a classifier. However most of the existing active learning strategies select only a single sample for labeling by the oracle in every iteration. This results in retraining the classifier after each sample is added which is quite computationally expensive. Also many of the existing sample selection strategies are not suitable for the multi-class classification tasks. To overcome these issues, we propose an efficient batch mode framework for active learning using the notion of influence sets based on Reverse Nearest Neighbor, which is applicable for multi-class classification as well. To demonstrate the effectiveness of our technique, we compare its performance against existing active learning techniques on real life datasets. Experimental results show that our technique outperforms existing active learning methods significantly especially on multi-class datasets.