In machine learning and applied fields of statistics, receiver operating characterization (ROC) analysis is commonly used to select optimal algorithms/models. However, at a lecture I once attended on mathematical optimization, I remember the lecturer saying that ROC analysis just wasn't considered a useful approach to optimization. No justification was offered. As a non-mathematician, I want to know (1) is this true that ROC analysis is not used in optimization problems and (2) if so, why?
Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. It's 100% free, no registration required.
Here's how it works:
- Anybody can ask a question
- Anybody can answer
- The best answers are voted up and rise to the top
The lecturer was wrong. It is now an active area of research (though outside my speciality). For instance, in a paper at NIPS in 2003, they discuss using ROC AUC instead of error as an objective function in training neural networks.