实验室关于聚类在极限学习机中的应用研究被期刊Multimedia Tools and Applications录用
新闻来源:IR实验室       发布时间:2019/10/12 10:12:40

  近日接到编辑部通知,博士生楚永贺的论文 "  Fuzzy ELM for classification based on feature space" 被Multimedia Tools and Applications录用。

摘要:极限学习机(ELM)作为一个有竞争力的机器学习算法,以其简单的理论和易于实施的特点在模式识别领域得到了广泛的应用。近来,针对噪音及离群数据研究人员提出了相关的研究算法,然而如何应用ELM在噪音情况下进行学习和识别仍是一个重要的研究课题。本文,我们提出了一种新的模糊极限学习机算法。本文在计算样本隶属度时考虑到特征映射空间对数据的影响,从而在特征映射空间计算样本的隶属度而非数据输入空间。该方法有效减弱了噪音及离群点对ELM分类性能的影响,显著提高了ELM算法的分类精度和泛化能力。通过在UCI数据集和文本数据集上进行实验,实验结果表明本文提出的算法显著提高了ELM的分类能力并优于其他算法。


Abstract: 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.