Mar 11, 2021 · First the model sequence is sorted by the model cost and predictive accuracy. Then, the model sequence is compressed such that those members ...

Jan 2, 2019 · Instead of outputting a single model, we produce a model schedule -- a list of models, sorted by model costs and expected predictive accuracy.

May 14, 2020 · Abstract. Many applications require the collection of data on different variables or measurements over many system performance metrics.

Nov 28, 2021 · Abstract. Many applications require the collection of data on different variables or measurements over many system performance metrics.

Instead of outputting a single model, we produce a model schedule—a list of models, sorted by model costs and expected predictive accuracy. This could be used ...

This could be used to choose the model with the best predictive accuracy under a given budget, or to trade off between the budget and the predictive accuracy.

This work proposes a computationally efficient approach that could find a near optimal model under a given budget by exploring the most 'promising' part of ...

This is a fairly new class of problems in the area of cost-sensitive learning. A few attempts have been made to incorporate costs in combining and selecting ...

Bibliographic details on Cost-sensitive Selection of Variables by Ensemble of Model Sequences.

Sep 28, 2021 · Cost-sensitive (CS) learning refers to aiming at minimising costs related to the dataset instead of error, typically via these costs influencing ...