Classification is essential to the applications in the field of data mining, artificial intelligence, and fault detection. There exists a strong need in developing accurate, suitable, and efficient classification methods and algorithms with broad applicability. Random forest is a general algorithm that is often used for classification under complex conditions. Although it has been widely adopted, its combination with diverse fuzzy theory is still worth exploring. In this paper, we propose the intuitionistic fuzzy random forest (IFRF), a new random forest ensemble of intuitionistic fuzzy decision trees (IFDT). Such trees in forest use intuitionistic fuzzy information gain to select features and consider hesitation in information transmission. The proposed method enjoys the power of the randomness from bootstrapped sampling and feature selection, the flexibility of fuzzy logic and fuzzy sets, and the robustness of multiple classifier systems. Extensive experiments demonstrate that the IFRF has competitative and superior performance compared to other state-of-the-art fuzzy and ensemble algorithms. IFDT is more suitable for ensemble learning with outstanding classification accuracy. This study is the first to propose a random forest ensemble based on the intuitionistic fuzzy theory.

翻译：分类是数据挖掘、人工智能和故障检测领域应用中的关键问题。目前迫切需要开发具有广泛适用性的精确、合适且高效的分类方法和算法。随机森林是一种在复杂条件下常用于分类的通用算法。尽管该算法已被广泛采用，但其与多种模糊理论的结合仍有待探索。本文提出了一种直觉模糊随机森林（IFRF），这是一种由直觉模糊决策树（IFDT）构成的新型随机森林集成方法。森林中的这些树采用直觉模糊信息增益选择特征，并考虑了信息传递中的犹豫度。所提方法兼具来自自助采样和特征选择的随机性、模糊逻辑与模糊集的灵活性，以及多分类器系统的鲁棒性。大量实验表明，与其他先进的模糊算法和集成算法相比，IFRF具有竞争性和优越的性能。IFDT凭借其卓越的分类准确率更适用于集成学习。本研究首次提出了基于直觉模糊理论的随机森林集成方法。