In graph-based active learning, algorithms based on expected error minimization (EEM) have been popular and yield good empirical performance.
The exact computation of EEM optimally balances exploration and exploitation.
In practice, however, EEM-based algorithms employ various approximations due to the computational hardness of exact EEM.
This can result in a lack of either exploration or exploitation, which can negatively impact the effectiveness of active learning.
We propose a new algorithm TSA (Two-Step Approximation) that balances between exploration and exploitation efficiently while enjoying the same computational complexity as existing approximations.
Finally, we empirically show the value of balancing between exploration and exploitation in both toy and real-world datasets where our method outperforms several state-of-the-art methods.