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首页> 外文期刊>BMC Bioinformatics >Learning by aggregating experts and filtering novices: a solution to crowdsourcing problems in bioinformatics
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Learning by aggregating experts and filtering novices: a solution to crowdsourcing problems in bioinformatics

机译：通过聚集专家和筛选新手来学习：解决生物信息学中众包问题的解决方案

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BackgroundIn many biomedical applications, there is a need for developing classification models based on noisy annotations. Recently, various methods addressed this scenario by relaying on unreliable annotations obtained from multiple sources.ResultsWe proposed a probabilistic classification algorithm based on labels obtained by multiple noisy annotators. The new algorithm is capable of eliminating annotations provided by novice labellers and of providing a more accurate estimate of the ground truth by consensus labelling according to higher quality annotations. The approach is evaluated on text classification and prediction of protein disorder. Our study suggests that the higher levels of accuracy, effectiveness and performance can be achieved by the new method as compared to alternatives.ConclusionsThe proposed method is applicable for meta-learning from multiple existing classification models and noisy annotations obtained by humans. It is particularly beneficial when many annotations are obtained by novice labellers. In addition, the proposed method can provide further characterization of each annotator that can help in developing more accurate classifiers by identifying the most competent annotators for each data instance.
机译：背景技术在许多生物医学应用中，需要开发基于嘈杂注释的分类模型。近年来，有多种方法通过中继从多个来源获得的不可靠注释来解决这种情况。结果我们提出了一种基于由多个噪声注释器获取的标签的概率分类算法。新算法能够消除由新手贴标人员提供的注释，并能够根据更高质量的注释通过共识性标签提供更准确的地面实况估计。该方法在文本分类和蛋白质失调的预测上进行了评估。我们的研究表明，与替代方法相比，该新方法可以实现更高水平的准确性，有效性和性能。结论该方法适用于从多个现有分类模型和人类获得的嘈杂注释中进行元学习。当新手贴标者获得许多注释时，这特别有益。另外，所提出的方法可以提供每个注释器的进一步表征，这可以通过为每个数据实例标识最有能力的注释器来帮助开发更准确的分类器。

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