In syntactic and structural pattern recognition, data represented as strings appear in several supervised classification applications. In some situations, data collections show imbalanced class distributions, which typically results in the classifier biasing its performance to the class representing the majority of objects. To solve this problem, some oversampling methods have been proposed for data represented as strings. However, this type of method has been little studied in the literature. Therefore, in this paper, we present an oversampling method for working in string space that balances the minority class and gets better classification results than state-of-the-art oversampling methods, especially for highly imbalanced problems. Furthermore, according to our experiments, the proposed method is much faster than those reported in the literature.

在句法和结构模式识别中，表示为字符串的数据出现在几个监督分类应用程序中。在某些情况下，数据集合显示出不平衡的类分布，这通常会导致分类器将其性能偏向代表大多数对象的类。为了解决这个问题，已经提出了一些对表示为字符串的数据的过采样方法。然而，这种类型的方法在文献中鲜有研究。因此，在本文中，我们提出了一种在字符串空间中工作的过采样方法，该方法可以平衡少数类并获得比最先进的过采样方法更好的分类结果，尤其是对于高度不平衡的问题。此外，根据我们的实验，