一种基于极限学习机的最小二乘误差加权投票方法

Jingjing Cao, S. Kwong, Ran Wang, Ke Li
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引用次数: 27

摘要

极限学习机(Extreme Learning Machine, ELM)因其速度快而成为解决分类问题的热门方法。然而,由于ELM系统的性能往往依赖于随机输入的隐藏节点参数,因此系统可能不可靠。为了提高单个分类器的可靠性和准确性,多分类器组合技术被广泛采用。因此,本文提出了一种基于最小二乘误差(MSE)的加权投票方法来优化多个elm的线性组合。在10个VCI数据集上的实验结果表明,该分类器的分类性能优于原始ELM和基于投票的ELM分类器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A weighted voting method using minimum square error based on Extreme Learning Machine
Extreme Learning Machine (ELM) has become popular for solving classification problem due to its fast speed. However, the system of ELM may be unreliable since its performance often relies on random input hidden node parameters. The techniques of combining multiple classifiers are widely adopted to improve both reliability and accuracy of a single classifier. Thus, this paper presents a minimum square error (MSE) based weighted voting method to optimize the linear combination of multiple ELMs. The experimental results over ten VCI data sets show better classification performance than the original ELM and the voting based ELM classifiers.
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