Max-Cost Discrete Function Evaluation Problem under a Budget
This work addresses a problem in clinical diagnosis and similar domains where minimizing maximum test costs is critical, offering incremental improvements through flexible impurity design.
The authors tackled the max-cost Discrete Function Evaluation Problem under budget constraints, developing a class of admissible impurity functions that provide O(log n) approximation guarantees for optimal cost among trees with specified classification accuracy levels.
We propose novel methods for max-cost Discrete Function Evaluation Problem (DFEP) under budget constraints. We are motivated by applications such as clinical diagnosis where a patient is subjected to a sequence of (possibly expensive) tests before a decision is made. Our goal is to develop strategies for minimizing max-costs. The problem is known to be NP hard and greedy methods based on specialized impurity functions have been proposed. We develop a broad class of \emph{admissible} impurity functions that admit monomials, classes of polynomials, and hinge-loss functions that allow for flexible impurity design with provably optimal approximation bounds. This flexibility is important for datasets when max-cost can be overly sensitive to "outliers." Outliers bias max-cost to a few examples that require a large number of tests for classification. We design admissible functions that allow for accuracy-cost trade-off and result in $O(\log n)$ guarantees of the optimal cost among trees with corresponding classification accuracy levels.