RNN Based Sampling Technique for Effective Active Learning

In this paper, we address the problem of active learning using the notion of influence sets based on Reverse Nearest Neighbor. Active learning is an area of machine learning which emphasizes on achieving optimal classification performance using as few labeled samples as possible. Reverse nearest neighbors have been used in domains such as clustering and outliers detection in the past effectively. In this paper, we devise a new sampling method for instances based on the knowledge provided from RNN influence sets. To demonstrate the effectiveness of our sampling method, we compare its performance against existing sampling methods on few real life datasets. The experimental results show that our technique outperforms existing methods, particularly on multi-class datasets.