High-quality data is crucial for accurate machine learning and actionable
analytics, however, mislabeled or noisy data is a common problem in many
domains. Distinguishing low- from high-quality data can be challenging, often
requiring expert knowledge and considerable manual intervention. Data Valuation
algorithms are a class of methods that seek to quantify the value of each
sample in a dataset based on its contribution or importance to a given
predictive task. These data values have shown an impressive ability to identify
mislabeled observations, and filtering low-value data can boost machine
learning performance. In this work, we present a simple alternative to existing
methods, termed Data Valuation with Gradient Similarity (DVGS). This approach
can be easily applied to any gradient descent learning algorithm, scales well
to large datasets, and performs comparably or better than baseline valuation
methods for tasks such as corrupted label discovery and noise quantification.
We evaluate the DVGS method on tabular, image and RNA expression datasets to
show the effectiveness of the method across domains. Our approach has the
ability to rapidly and accurately identify low-quality data, which can reduce
the need for expert knowledge and manual intervention in data cleaning tasks.

通过梯度相似性进行数据估值的方法在识别低质量数据方面表现出良好的效果，并能减少对专业知识和手动干预的需求。

梯度相似性数据评估

Data Valuation with Gradient Similarity

Labeling mistakes are frequently encountered in real-world applications. If
not treated well, the labeling mistakes can deteriorate the classification
performances of a model seriously. To address this issue, we propose an
improved Naive Bayes method for text classification. It is analytically simple
and free of subjective judgements on the correct and incorrect labels. By
specifying the generating mechanism of incorrect labels, we optimize the
corresponding log-likelihood function iteratively by using an EM algorithm. Our
simulation and experiment results show that the improved Naive Bayes method
greatly improves the performances of the Naive Bayes method with mislabeled
data.

提出了一种改进的朴素贝叶斯文本分类方法，通过使用 EM 算法迭代优化对应的对数似然函数并明确不正确标签的生成机制，大大提高了带有错误标签数据的朴素贝叶斯方法的性能表现。