Toward Optimal Active Learning through Monte Carlo Estimation of Error Reduction
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These methods are popular because for many learning models, closed form calculation of the expected future error is intractable. Our approach is made feasible by taking a Monte Carlo approach to estimating the expected reduction in error due to the labeling of a query. In experimental results on three real-world data sets we reach high accuracy with four times fewer labelled examples than competing methods.