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.
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Latest papers
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms
The package is open source and can be installed through PyPI.
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.
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.