论文标题
使用变质真相和归因反馈来降级自动编码器的多重插补
Multiple Imputation with Denoising Autoencoder using Metamorphic Truth and Imputation Feedback
论文作者
论文摘要
尽管数据可能很丰富，但由于缺少列或行，完整的数据较少。这种丢失破坏了下游数据产品的性能，这些数据产品忽略了不完整的情况或创建派生的已完成数据以进行后续处理。为了完全利用和正确使用数据，需要适当管理缺少数据。我们建议使用denoising自动编码器来学习数据的内部表示形式。此外，我们使用变质真理和归因反馈的新型机制来维持属性的统计完整性并消除学习过程中的偏见。我们的方法探讨了插补对丢失数据的各种缺失机制和模式的影响，在许多标准测试用例中表现优于其他方法。
Although data may be abundant, complete data is less so, due to missing columns or rows. This missingness undermines the performance of downstream data products that either omit incomplete cases or create derived completed data for subsequent processing. Appropriately managing missing data is required in order to fully exploit and correctly use data. We propose a Multiple Imputation model using Denoising Autoencoders to learn the internal representation of data. Furthermore, we use the novel mechanisms of Metamorphic Truth and Imputation Feedback to maintain statistical integrity of attributes and eliminate bias in the learning process. Our approach explores the effects of imputation on various missingness mechanisms and patterns of missing data, outperforming other methods in many standard test cases.
