人工预测市场分类导论
An Introduction to Artificial Prediction Markets for Classification
摘要：
预测市场在现实生活中被用来预测总统选举等重要结果。本文提出了一种人工预测市场的数学理论，用于条件概率估计量的监督学习。人工预测市场是一种融合特征或训练分类器的预测信息的新方法，融合结果是可能结果的合同价格。通过使用培训实例更新参与者的预算，可以在线培训市场。受真实预测市场的启发，管理市场的方程来自简单而合理的假设。有效的数值算法求解这些方程。所得到的人工预测市场是一个极大似然估计。推广了Boosting和随机森林中的线性聚集方法，以及逻辑斯谛回归和一些核方法。此外，市场机制允许将只在特定情况下参与的专门分类器集合起来。实验比较表明，人工预测市场在合成数据和真实UCI数据集上的表现往往优于随机森林和隐式在线学习。此外，对CT数据中盆腔和腹部淋巴结检测的广泛评估表明，预测市场将adaboost的检测率从79.6%提高到81.2%，3个假阳性/体积。
英文摘要：
Prediction markets are used in real life to predict outcomes of interest such as presidential elections. This paper presents a mathematical theory of artificial prediction markets for supervised learning of conditional probability estimators. The artificial prediction market is a novel method for fusing the prediction information of features or trained classifiers, where the fusion result is the contract price on the possible outcomes. The market can be trained online by updating the participants' budgets using training examples. Inspired by the real prediction markets, the equations that govern the market are derived from simple and reasonable assumptions. Efficient numerical algorithms are presented for solving these equations. The obtained artificial prediction market is shown to be a maximum likelihood estimator. It generalizes linear aggregation, existent in boosting and random forest, as well as logistic regression and some kernel methods. Furthermore, the market mechanism allows the aggregation of specialized classifiers that participate only on specific instances. Experimental comparisons show that the artificial prediction markets often outperform random forest and implicit online learning on synthetic data and real UCI datasets. Moreover, an extensive evaluation for pelvic and abdominal lymph node detection in CT data shows that the prediction market improves adaboost's detection rate from 79.6% to 81.2% at 3 false positives/volume.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
