Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy

机译：主动学习和有效的特征加权方法，可提高数据质量和分类精度

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Many machine learning datasets are noisy with a substantial number of mislabeled instances. This noise yields sub-optimal classification performance. In this paper we study a large, low quality annotated dataset, created quickly and cheaply using Amazon Mechanical Turk to crowd-source annotations. We describe computationally cheap feature weighting techniques and a novel non-linear distribution spreading algorithm that can be used to it-eratively and interactively correcting mis-labeled instances to significantly improve annotation quality at low cost. Eight different emotion extraction experiments on Twitter data demonstrate that our approach is just as effective as more computationally expensive techniques. Our techniques save a considerable amount of time.

机译：许多机器学习数据集嘈杂，带有大量错误标记的实例。这种噪声会产生次优的分类性能。在本文中，我们研究了一个大型的，低质量的带注释的数据集，该数据集是使用Amazon Mechanical Turk快速廉价地创建的，用于众包注释。我们描述了计算上便宜的特征加权技术和一种新颖的非线性分布扩展算法，该算法可用于迭代和交互地纠正误贴标签的实例，从而以低成本显着提高注释质量。 Twitter数据上的八个不同的情感提取实验表明，我们的方法与更昂贵的计算技术一样有效。我们的技术可节省大量时间。

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《Annual meeting of the Association for Computational Linguistics》|2014年|1104-1112|共9页
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Justin Martineau; Lu Chen; Doreen Cheng; Amit Sheth;
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机译：主动学习与高效的特征加权方法，以提高数据质量和分类准确性

Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy

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