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ztt_fzu

铁虫 (初入文坛)

[求助] 文章刚刚发表, 各位大侠 能不能帮忙查一下 是否被SCI收录?

论文:Instance Selection For Time Series Classification Based On Immune Binary Particle Swarm Optimization. Knowledge-Based Systems, Volume 49, September 2013, Pages 106–115。  各位大侠 能不能帮忙查一下 是否被SCI收录?
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山海一色

木虫 (著名写手)

【答案】应助回帖

感谢参与,应助指数 +1
祝贺楼主,你所投稿的杂志Knowledge-Based Systems是核心板SCI影响因子4.104,很高吗,一定被检索的,放心吧。
4楼2013-09-18 00:21:18
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henrylc

金虫 (正式写手)

【答案】应助回帖

★ ★ ★ ★
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ztt_fzu: 金币+4, ★★★★★最佳答案 2013-09-18 07:07:00
恭喜楼主,已经检索了


Instance selection for time series classification based on immune binary particle swarm optimization  
作者: Zhai, TT (Zhai, Tingting)[ 1 ] ; He, ZF (He, Zhenfeng)[ 1 ]  
来源出版物: KNOWLEDGE-BASED SYSTEMS  卷: 49   页: 106-115   DOI: 10.1016/j.knosys.2013.04.021   出版年: SEP 2013  
被引频次: 0 (来自 Web of Science)  
引用的参考文献: 34      [ 查看 Related Records ]     引证关系图      
摘要: We propose a new immune binary particle swarm optimization algorithm (IBPSO) to solve the problem of instance selection for time series classification, whose objective is to find out the smallest instance combination with maximal classification accuracy. The proposed IBPSO is based on the basic binary particle swarm optimization (BPSO) algorithm proposed by Kennedy and Eberhart. Its immune mechanism includes vaccination and immune selection. Vaccination employs the hubness score of time series and the particles' inertance as heuristic information to direct the search process. Immune selection procedure always discards the particle with the worst fitness in the current swarm for preventing the degradation of the swarm. Experimental results on small and medium datasets show that IBPSO outperforms BPSO and deterministic INSIGHT in terms of storage requirement and classification accuracy, and presents better robustness to noise than BPSO. In addition, experimental results on larger datasets indicate that IBPSO has better scalability than BPSO. (C) 2013 Elsevier B.V. All rights reserved.  
入藏号: WOS:000322428100010  
文献类型: Article  
语种: English  
作者关键词: Instance selection; Time series classification; Binary particle swarm optimization; Immune algorithm; Data reduction  
KeyWords Plus: LEARNING ALGORITHMS; REDUCTION  
通讯作者地址: Zhai, TT (通讯作者) Fuzhou Univ, Dept Math & Comp Sci, Fuzhou 350002, Fujian, Peoples R China.
  增强组织信息的名称
    Fuzhou University  

地址:  [ 1 ] Fuzhou Univ, Dept Math & Comp Sci, Fuzhou 350002, Fujian, Peoples R China
  增强组织信息的名称
    Fuzhou University  

电子邮件地址: ztt19881001@sina.com  
出版商: ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS  
Web of Science 类别: Computer Science, Artificial Intelligence  
研究方向: Computer Science  
IDS 号: 191QQ  
ISSN: 0950-7051
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2楼2013-09-17 21:32:26
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successfulsbl

木虫 (职业作家)

【答案】应助回帖


感谢参与,应助指数 +1
ztt_fzu: 金币+1, ★★★很有帮助 2013-09-18 07:07:11
已确定被收录,恭喜你啊
3楼2013-09-17 21:58:29
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