Predicting accurate probabilities with a ranking loss
Machine Learning
2012-06-22 v1 Machine Learning
Abstract
In many real-world applications of machine learning classifiers, it is essential to predict the probability of an example belonging to a particular class. This paper proposes a simple technique for predicting probabilities based on optimizing a ranking loss, followed by isotonic regression. This semi-parametric technique offers both good ranking and regression performance, and models a richer set of probability distributions than statistical workhorses such as logistic regression. We provide experimental results that show the effectiveness of this technique on real-world applications of probability prediction.
Cite
@article{arxiv.1206.4661,
title = {Predicting accurate probabilities with a ranking loss},
author = {Aditya Menon and Xiaoqian Jiang and Shankar Vembu and Charles Elkan and Lucila Ohno-Machado},
journal= {arXiv preprint arXiv:1206.4661},
year = {2012}
}
Comments
ICML2012