Feature selection plays a crucial role in machine learning, as it eliminates data noise and redundancy, thereby significantly reducing computational complexity and enhancing the overall performance of the model. The challenges of feature selection for hybrid information systems stem from the difficulty in quantifying the disparities among nominal attribute values. Furthermore, a significant majority of the current methodologies exhibit sensitivity to noise. This paper introduces techniques that address the aforementioned issues from the perspective of fuzzy evidence theory. First of all, a new distance incorporating decision attributes is defined, and then a relation between fuzzy evidence theory and fuzzy \(\beta \) covering with an anti-noise mechanism is established. In this framework, two robust feature selection algorithms for hybrid data are proposed based on fuzzy belief and fuzzy plausibility. Experiments on 10 data sets of various types show that compared with the other 6 state-of-the-art algorithms, the proposed algorithms improve the anti-noise ability by at least 6% with higher average classification accuracy. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability.

特征选择在机器学习中起着至关重要的作用，因为它消除了数据噪声和冗余，从而显着降低计算复杂度并提高模型的整体性能。混合信息系统特征选择的挑战源于量化名义属性值之间差异的困难。此外，绝大多数当前方法都表现出对噪声的敏感性。本文从模糊证据理论的角度介绍了解决上述问题的技术。首先定义了一个包含决策属性的新距离，然后建立了模糊证据理论与具有抗噪声机制的模糊\(\beta \)覆盖之间的关系。在此框架中，提出了两种基于模糊置信度和模糊似然性的鲁棒混合数据特征选择算法。在10个不同类型的数据集上进行的实验表明，与其他6种最先进的算法相比，该算法的抗噪声能力提高了至少6%，平均分类精度更高。因此，可以得出结论，所提出的算法具有优异的抗噪声能力，同时保持良好的特征选择能力。