3 papers with code • 0 benchmarks • 0 datasets
The task is to classify the dataset with costly features with different budget settings. The final metric is the normalized area under the cost-accuracy curve.
This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget.
We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost.