Noise negatively affects the complexity and performance of models built in classification problems. The most common approach to mitigate its consequences is the usage of preprocessing techniques, known as noise filters, which are designed to remove noisy samples from the training data. Nevertheless, they are specifically oriented to deal with errors affecting class labels. Their employment may not always result in an improvement when noise affects attribute values. In these cases, correcting the errors is an interesting alternative to traditional noise filtering that has not been enough studied so far in the specialized literature. This research proposes an attribute noise correction method with the final aim of increasing the performance of the classification algorithms used later. The identification of noisy data is based on an error score assigned to each one of the attribute values in the dataset, which are then passed through an optimization process to correct their potential noise. The validity of the proposed method is studied in an exhaustive experimental study, in which it is compared to several well-known preprocessing methods to deal with noisy datasets. The results obtained show the suitability of attribute noise correction with respect to the other alternatives when data suffer from attribute noise.

噪声会对内置于分类问题的模型的复杂性和性能产生负面影响。减轻其后果的最常见方法是使用预处理技术，称为噪声过滤器，旨在从训练数据中去除噪声样本。然而，它们专门用于处理影响类标签的错误。当噪声影响属性值时，它们的使用可能并不总是导致改进。在这些情况下，纠正错误是一种有趣的替代传统噪声过滤的方法，迄今为止在专业文献中还没有得到足够的研究。本研究提出了一种属性噪声校正方法，最终目的是提高以后使用的分类算法的性能。噪声数据的识别基于分配给数据集中每个属性值的错误分数，然后通过优化过程来纠正其潜在噪声。在详尽的实验研究中研究了所提出方法的有效性，其中将其与处理噪声数据集的几种众所周知的预处理方法进行了比较。获得的结果表明，当数据受到属性噪声影响时，属性噪声校正相对于其他替代方案的适用性。其中将它与几种众所周知的预处理方法进行比较以处理嘈杂的数据集。获得的结果表明，当数据受到属性噪声影响时，属性噪声校正相对于其他替代方案的适用性。其中将它与几种众所周知的预处理方法进行比较以处理嘈杂的数据集。获得的结果表明，当数据受到属性噪声影响时，属性噪声校正相对于其他替代方案的适用性。