排序预测的无监督评价与加权集结
Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions
摘要：
从不同基础分类器的集合中聚合预测的学习算法始终优于单个方法。这些策略中的许多已经在监督设置中开发，其中每个基础分类器的准确性可以凭经验测量，并且这些信息被纳入训练过程中。然而，对标记数据的依赖排除了集成方法应用于许多现实世界的问题，标记数据尚未策划。为此，我们开发了一个新的理论框架，二进制分类，无监督的多方法聚合（SUMMA）的战略，估计性能的基础分类器和集成学习的最佳策略，从未标记的数据。
英文摘要：
Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods. Many of these strategies have been developed in a supervised setting, where the accuracy of each base classifier can be empirically measured and this information is incorporated in the training process. However, the reliance on labeled data precludes the application of ensemble methods to many real world problems where labeled data has not been curated. To this end we developed a new theoretical framework for binary classification, the Strategy for Unsupervised Multiple Method Aggregation (SUMMA), to estimate the performances of base classifiers and an optimal strategy for ensemble learning from unlabeled data.
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。
