Predicting accurate probabilities with a ranking loss
Predicting accurate probabilities with a ranking loss
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发表时间：
2012-06
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通讯作者：
A. Menon;Xiaoqian Jiang;Shankar Vembu;C. Elkan;L. Ohno-Machado
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作者：
A. Menon;Xiaoqian Jiang;Shankar Vembu;C. Elkan;L. Ohno-Machado
在机器学习分类器的许多实际应用中，预测样本属于特定类的概率是至关重要的。本文提出了一种简单的技术，预测概率的基础上优化排名损失，其次是保序回归。这种半参数技术提供了良好的排名和回归性能，并且比统计工具（如逻辑回归）更丰富的概率分布集。我们提供的实验结果表明，这种技术在现实世界中的应用概率预测的有效性。
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.