Classification with Costly Features
4 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.
Benchmarks
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Most implemented papers
Classification with Costly Features as a Sequential Decision-Making Problem
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.
Classification with Costly Features using Deep Reinforcement Learning
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.
Classification with Costly Features in Hierarchical Deep Sets
In this work, we extend an existing deep reinforcement learning-based algorithm with hierarchical deep sets and hierarchical softmax, so that it can directly process this data.
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms
The package is open source and can be installed through PyPI.