Class binarization techniques are used to decompose multi-class problems into several easier-to-solve binary sub-problems. One of the most popular binarization techniques is One versus One (OVO), which creates a sub-problem for each pair of classes of the original problem. Different versions of OVO have been developed to try to solve some of its problems, such as DYNOVO, which dynamically tries to select the best classifier for each sub-problem. In this paper, a new extension that has been made for DYNOVO, called PSEUDOVO, is presented. This extension also tries to avoid the non-competent sub-problems. An empirical study has been carried out over several UCI data sets, as well as a new data set of musical pieces of well-known classical composers. Promising results have been obtained, from which can be concluded that the PSEUDOVO extension improves the performance of DYNOVO.

类二值化技术用于将多类问题分解为几个易于解决的二进制子问题。最受欢迎的二值化技术之一是“一对一”（OVO），它为原始问题的每对类别创建了一个子问题。已经开发了不同版本的OVO来尝试解决其一些问题，例如DYNOVO，它动态地尝试为每个子问题选择最佳分类器。本文介绍了为DYNOVO进行的新扩展，称为PSEUDOVO。此扩展也试图避免不合格的子问题。对几个UCI数据集以及著名古典作曲家音乐作品的新数据集进行了实证研究。获得了可喜的结果，