| 6 | 1/1 | 返回列表 |
| 查看: 725 | 回復(fù): 5 | ||
ztt_fzu鐵蟲 (初入文壇)
|
[求助]
文章剛剛發(fā)表, 各位大俠 能不能幫忙查一下 是否被SCI收錄?
|
| 論文:Instance Selection For Time Series Classification Based On Immune Binary Particle Swarm Optimization. Knowledge-Based Systems, Volume 49, September 2013, Pages 106–115。 各位大俠 能不能幫忙查一下 是否被SCI收錄? |
金蟲 (正式寫手)
|
恭喜樓主,已經(jīng)檢索了 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 ] 來源出版物: KNOWLEDGE-BASED SYSTEMS 卷: 49 頁(yè): 106-115 DOI: 10.1016/j.knosys.2013.04.021 出版年: SEP 2013 被引頻次: 0 (來自 Web of Science) 引用的參考文獻(xiàn): 34 [ 查看 Related Records ] 引證關(guān)系圖 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. 入藏號(hào): WOS:000322428100010 文獻(xiàn)類型: Article 語(yǔ)種: English 作者關(guān)鍵詞: Instance selection; Time series classification; Binary particle swarm optimization; Immune algorithm; Data reduction KeyWords Plus: LEARNING ALGORITHMS; REDUCTION 通訊作者地址: Zhai, TT (通訊作者) Fuzhou Univ, Dept Math & Comp Sci, Fuzhou 350002, Fujian, Peoples R China. 增強(qiáng)組織信息的名稱 Fuzhou University 地址: [ 1 ] Fuzhou Univ, Dept Math & Comp Sci, Fuzhou 350002, Fujian, Peoples R China 增強(qiáng)組織信息的名稱 Fuzhou University 電子郵件地址: ztt19881001@sina.com 出版商: ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS Web of Science 類別: Computer Science, Artificial Intelligence 研究方向: Computer Science IDS 號(hào): 191QQ ISSN: 0950-7051 |

木蟲 (職業(yè)作家)
木蟲 (著名寫手)
|
銅蟲 (初入文壇)
| 6 | 1/1 | 返回列表 |
| 最具人氣熱帖推薦 [查看全部] | 作者 | 回/看 | 最后發(fā)表 | |
|---|---|---|---|---|
|
[考研] 材料與化工(0856)304求 B區(qū) 調(diào)劑 +3 | 邱gl 2026-03-21 | 3/150 |
|
|---|---|---|---|---|
|
[考研] 279求調(diào)劑 +4 | 紅衣隱官 2026-03-21 | 4/200 |
|
|
[考研] 材料 271求調(diào)劑 +3 | 展信悅_ 2026-03-21 | 3/150 |
|
|
[考研] 一志愿西安交通大學(xué)材料工程專業(yè) 282分求調(diào)劑 +7 | 楓橋ZL 2026-03-18 | 9/450 |
|
|
[考研] 299求調(diào)劑 +6 | △小透明* 2026-03-17 | 6/300 |
|
|
[考研] 311求調(diào)劑 +5 | 冬十三 2026-03-18 | 5/250 |
|
|
[考研] 274求調(diào)劑 +10 | S.H1 2026-03-18 | 10/500 |
|
|
[考研] 321求調(diào)劑 +9 | 何潤(rùn)采123 2026-03-18 | 11/550 |
|
|
[考研] 考研調(diào)劑求學(xué)校推薦 +3 | 伯樂29 2026-03-18 | 5/250 |
|
|
[考研] 287求調(diào)劑 +7 | 晨昏線與星海 2026-03-19 | 8/400 |
|
|
[考研] 中南大學(xué)化學(xué)學(xué)碩337求調(diào)劑 +3 | niko- 2026-03-19 | 6/300 |
|
|
[考研] 一志愿福大288有機(jī)化學(xué),求調(diào)劑 +3 | 小木蟲200408204 2026-03-18 | 3/150 |
|
|
[考研] 085600材料與化工求調(diào)劑 +6 | 緒幸與子 2026-03-17 | 6/300 |
|
|
[考研] 材料專碩306英一數(shù)二 +10 | z1z2z3879 2026-03-16 | 13/650 |
|
|
[碩博家園] 湖北工業(yè)大學(xué) 生命科學(xué)與健康學(xué)院-課題組招收2026級(jí)食品/生物方向碩士 +3 | 1喜春8 2026-03-17 | 5/250 |
|
|
[考研] 275求調(diào)劑 +4 | 太陽(yáng)花天天開心 2026-03-16 | 4/200 |
|
|
[考研] 302求調(diào)劑 +4 | 小賈同學(xué)123 2026-03-15 | 8/400 |
|
|
[考研] [導(dǎo)師推薦]西南科技大學(xué)國(guó)防/材料導(dǎo)師推薦 +3 | 尖角小荷 2026-03-16 | 6/300 |
|
|
[考研] 一志愿211 0703方向310分求調(diào)劑 +3 | 努力奮斗112 2026-03-15 | 3/150 |
|
|
[考研] 0856專碩279求調(diào)劑 +5 | 加油加油!? 2026-03-15 | 5/250 |
|