多源分层预报合并
Multi-source Hierarchical Prediction Consolidation
摘要：
在医疗数据采掘等大数据应用中，出于隐私考虑，需要从多个信息源收集同一实例的预测，在聚合多个预测时，原始特征会被丢弃或保留。此外，需要聚合众包标签以估计数据的真实情况。由于预测模型或人类众包工作人员的不完善，嘈杂和相互冲突的信息无处不在，不可避免。尽管已经提出了最先进的聚合方法来处理具有平面结构的标签空间，但是随着标签空间变得越来越复杂，标签分层结构下的聚合变得必要，但是在很大程度上被忽略了。这些标签层次结构可以提供相当多的信息，因为它们通常由领域专家创建，以理解许多现实世界案例（如蛋白质功能相互作用或疾病关系）的高度复杂的标签相关性。我们提出了一种新的多源分层预测合并方法，有效地利用复杂的分层标签结构，以解决噪声和冲突的信息，本质上源于多个不完美的来源。我们制定的问题作为一个封闭形式的解决方案的优化问题。所提出的方法捕获的平滑度整体信息源，以及惩罚任何合并结果，违反了从标签层次结构的约束。层次实例相似性以及合并结果以完全无监督的迭代方式推断。在合成数据集和真实数据集上的实验结果表明了该方法的有效性。
英文摘要：
In big data applications such as healthcare data mining, due to privacy concerns, it is necessary to collect predictions from multiple information sources for the same instance, with raw features being discarded or withheld when aggregating multiple predictions. Besides, crowd-sourced labels need to be aggregated to estimate the ground truth of the data. Because of the imperfect predictive models or human crowdsourcing workers, noisy and conflicting information is ubiquitous and inevitable. Although state-of-the-art aggregation methods have been proposed to handle label spaces with flat structures, as the label space is becoming more and more complicated, aggregation under a label hierarchical structure becomes necessary but has been largely ignored. These label hierarchies can be quite informative as they are usually created by domain experts to make sense of highly complex label correlations for many real-world cases like protein functionality interactions or disease relationships. We propose a novel multi-source hierarchical prediction consolidation method to effectively exploits the complicated hierarchical label structures to resolve the noisy and conflicting information that inherently originates from multiple imperfect sources. We formulate the problem as an optimization problem with a closed-form solution. The proposed method captures the smoothness overall information sources as well as penalizing any consolidation result that violates the constraints derived from the label hierarchy. The hierarchical instance similarity, as well as the consolidation result, are inferred in a totally unsupervised, iterative fashion. Experimental results on both synthetic and real-world datasets show the effectiveness of the proposed method over existing alternatives.
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。
