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张宪超
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教授   博士生导师   硕士生导师

主要任职: 国防(先进)科学技术发展研究院副院长

性别: 男

毕业院校: 中国科技大学

学位: 博士

在职信息:在职

所在单位: 软件学院

学科: 计算机应用技术 软件工程

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A Novel Extreme Learning Machine-Based Classification Algorithm for Uncertain Data

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论文类型: 会议论文

第一作者: Zhang, Xianchao

合写作者: Sun, Daoyuan,Li, Yuangang,Liu, Han,Liang, Wenxin

发表时间: 2017-01-01

发表刊物: TRENDS AND APPLICATIONS IN KNOWLEDGE DISCOVERY AND DATA MINING, 2017

收录刊物: EI、CPCI-S

卷号: 10526

页面范围: 176-188

关键字: Extreme learning machine; Uncertain data; Classification

摘要: Traditional classification algorithms are widely used on determinate data. However, uncertain data is ubiquitous in many real applications, which poses a great challenge to traditional classification algorithms. Extreme learning machine (ELM) is a traditional and powerful classification algorithm. However, existing ELM-based uncertain data classification algorithms can not deal with data uncertainty well. In this paper, we propose a novel ELM-based uncertain data classification algorithm, called UELM. UELM firstly employs exact probability density function (PDF) instead of expected values or sample points to model uncertain data, thus avoiding the loss of uncertain information (probability distribution information of uncertain data). Furthermore, UELM redesigns the traditional ELM algorithm by modifying the received content of input layer and the activation function of hidden layer, thus making the ELM algorithm more applicable to uncertain data. Extensive experimental results on different datasets show that our proposed UELM algorithm outperforms the baselines in accuracy and efficiency.

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