Lots of real-world problems require multiclass classification. Since most general classification methods are originally introduced for binary problems (including two classes), they should be extended to multiclass problems. A solution proposed for multiclass problems is to decompose such problems to several binary ones and then combine the results obtained from smaller problems as a tree-based structure to obtain the final solution. In this study, a novel method which uses VlseKriterijumska optimizacija I Kompromisno Resenje multi-criteria decision-making was proposed to build the best directed binary tree with minimum error. The proposed method is independent of classifier; nevertheless, in the current experiments, the support vector machine was employed as the base classifier. The proposed method was tested on datasets and the results were compared with other methods. It can be seen that it improves precision of predictions significantly.

中文翻译：

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基于多准则决策的多类分类

许多现实世界的问题需要多类分类。由于大多数通用分类方法最初是针对二元问题（包括两个类）引入的，因此应该将它们扩展到多类问题。针对多类问题提出的解决方案是将这些问题分解为几个二元问题，然后将从小问题中获得的结果组合为基于树的结构，以获得最终解决方案。在这项研究中，提出了一种使用 VlseKriterijumska optimizacija I Kompromisno Resenje 多准则决策的新方法来构建具有最小误差的最佳定向二叉树。所提出的方法独立于分类器；尽管如此，在当前的实验中，支持向量机被用作基分类器。所提出的方法在数据集上进行了测试，并将结果与​​其他方法进行了比较。可以看出，它显着提高了预测的精度。