As a competitive machine learning algorithm, extreme learning machine (ELM), with its simple theory and easy implementation, has been widely used in the field of pattern accuracy. Recently, researchers have proposed related research algorithms to accommodate noise and outlier data. With a proper fuzzy membership function, a fuzzy ELM can effectively reduce the effects of outliers when solving the classification problem. However, how to apply ELM for learning and accuracy in the presence of noise is still an important research topic. A novel fuzzy ELM (ANFELM) technique is proposed in this paper. In the algorithm, the membership degree of the sample is calculated in a feature mapping space instead of the data input space. The algorithm provides good performance in reducing the effects of outliers and significantly improves classification accuracy and generalization. Experiments on UCI datasets and textual datasets show that the proposed algorithm significantly improves the classification capability of ELM and is superior to other algorithms.

作为一种竞争性的机器学习算法，极限学习机（ELM）具有简单的理论和易于实现的方法，已在图案精度领域广泛使用。最近，研究人员提出了相关的研究算法，以适应噪声和异常数据。借助适当的模糊隶属函数，模糊ELM在解决分类问题时可以有效地减少离群值的影响。然而，如何在存在噪声的情况下将ELM应用于学习和准确性仍然是重要的研究课题。提出了一种新颖的模糊ELM（ANFELM）技术。在该算法中，样本的隶属度是在特征映射空间而不是数据输入空间中计算的。该算法在减少离群值的影响方面提供了良好的性能，并显着提高了分类精度和泛化能力。在UCI数据集和文本数据集上的实验表明，该算法显着提高了ELM的分类能力，优于其他算法。