论文标题
用于分类问题的非侵入性更正算法损坏的数据
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data
论文作者
论文摘要
提出了一种新颖的校正算法，用于用于损坏的培训数据的多类分类问题。从某种意义上说，该算法是非侵入性的，因为它通过在模型预测中添加校正过程来后处理受过训练的分类模型。校正过程可以与任何近似器相结合，例如逻辑回归，各种体系结构的神经网络等。当训练数据集足够大时，我们证明校正后的模型可以提供正确的分类结果，就像训练数据中没有损坏一样。对于有限大小的数据集，与没有校正算法的模型相比，校正的模型产生的恢复结果明显更好。本文中的所有理论发现均通过我们的数值示例来验证。
A novel correction algorithm is proposed for multi-class classification problems with corrupted training data. The algorithm is non-intrusive, in the sense that it post-processes a trained classification model by adding a correction procedure to the model prediction. The correction procedure can be coupled with any approximators, such as logistic regression, neural networks of various architectures, etc. When training dataset is sufficiently large, we prove that the corrected models deliver correct classification results as if there is no corruption in the training data. For datasets of finite size, the corrected models produce significantly better recovery results, compared to the models without the correction algorithm. All of the theoretical findings in the paper are verified by our numerical examples.
