Estimation of the Future Earthquake Situation by Using Neural Networks Ensemble
Earthquakes will do great harms to the people, to estimate the future earthquake situation in Chinese mainland is still an open issue. There have been previous attempts to solve this problem by using artificial neural networks. In this paper, a novel algorithm named MIFEB is proposed to improve the...
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| creator | Liu, Tian-Yu Li, Guo-Zheng Liu, Yue Wu, Geng-Feng Wang, Wei |
| description | Earthquakes will do great harms to the people, to estimate the future earthquake situation in Chinese mainland is still an open issue. There have been previous attempts to solve this problem by using artificial neural networks. In this paper, a novel algorithm named MIFEB is proposed to improve the estimation accuracy by combing bagging of neural networks with mutual information based feature selection for its individuals. MIFEB is compared with the general case of bagging on UCI data sets, then, MIFEB is used to forecast the seismicity of strong earthquakes in Chinese mainland, computation results show that MIFEB obtains higher accuracy than other several methods like bagging of neural networks and single neural networks do. |
| doi_str_mv | 10.1007/11760191_179 |
| format | Book Chapter |
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There have been previous attempts to solve this problem by using artificial neural networks. In this paper, a novel algorithm named MIFEB is proposed to improve the estimation accuracy by combing bagging of neural networks with mutual information based feature selection for its individuals. MIFEB is compared with the general case of bagging on UCI data sets, then, MIFEB is used to forecast the seismicity of strong earthquakes in Chinese mainland, computation results show that MIFEB obtains higher accuracy than other several methods like bagging of neural networks and single neural networks do.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540344827</identifier><identifier>ISBN: 3540344829</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540344834</identifier><identifier>EISBN: 3540344837</identifier><identifier>DOI: 10.1007/11760191_179</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Feature Selection ; Feature Selection Method ; Mutual Information ; Neural Network ; Strong Earthquake</subject><ispartof>Advances in Neural Networks - ISNN 2006, 2006, p.1231-1236</ispartof><rights>Springer-Verlag Berlin Heidelberg 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><relation>Lecture Notes in Computer Science</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11760191_179$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11760191_179$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>775,776,780,789,27902,38232,41418,42487</link.rule.ids></links><search><contributor>Zurada, Jacek M.</contributor><contributor>Lu, Bao-Liang</contributor><contributor>Yi, Zhang</contributor><contributor>Yin, Hujun</contributor><contributor>Wang, Jun</contributor><creatorcontrib>Liu, Tian-Yu</creatorcontrib><creatorcontrib>Li, Guo-Zheng</creatorcontrib><creatorcontrib>Liu, Yue</creatorcontrib><creatorcontrib>Wu, Geng-Feng</creatorcontrib><creatorcontrib>Wang, Wei</creatorcontrib><title>Estimation of the Future Earthquake Situation by Using Neural Networks Ensemble</title><title>Advances in Neural Networks - ISNN 2006</title><description>Earthquakes will do great harms to the people, to estimate the future earthquake situation in Chinese mainland is still an open issue. There have been previous attempts to solve this problem by using artificial neural networks. In this paper, a novel algorithm named MIFEB is proposed to improve the estimation accuracy by combing bagging of neural networks with mutual information based feature selection for its individuals. MIFEB is compared with the general case of bagging on UCI data sets, then, MIFEB is used to forecast the seismicity of strong earthquakes in Chinese mainland, computation results show that MIFEB obtains higher accuracy than other several methods like bagging of neural networks and single neural networks do.</description><subject>Feature Selection</subject><subject>Feature Selection Method</subject><subject>Mutual Information</subject><subject>Neural Network</subject><subject>Strong Earthquake</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540344827</isbn><isbn>3540344829</isbn><isbn>9783540344834</isbn><isbn>3540344837</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2006</creationdate><recordtype>book_chapter</recordtype><sourceid/><recordid>eNpVkM1OwzAQhM2fRCi98QA-IwV2s04cH1GVAlJFD9BzZBObhqQJxI5Q356gcoDTp9GMRqNh7ArhBgHkLaLMABWWKNURmyuZUyqAhMhJHLMIM8SYSKiTf14iT1kEBEmspKBzduH9OwAkUiURWxc-1Dsd6r7jveNha_lyDONgeaGHsP0cdWP5cx3GQ8Ts-cbX3Rt_suOg2wnhqx8az4vO251p7SU7c7r1dv7LGdssi5fFQ7xa3z8u7laxRwUhzhGkNSp3qJJXgwagspDLShthEW3qSOrUpSYhpMoJM2nhRO6MkJiRUzRj14de_zFMe-xQmr5vfIlQ_lxV_r2KvgE2YFa1</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Liu, Tian-Yu</creator><creator>Li, Guo-Zheng</creator><creator>Liu, Yue</creator><creator>Wu, Geng-Feng</creator><creator>Wang, Wei</creator><general>Springer Berlin Heidelberg</general><scope/></search><sort><creationdate>2006</creationdate><title>Estimation of the Future Earthquake Situation by Using Neural Networks Ensemble</title><author>Liu, Tian-Yu ; Li, Guo-Zheng ; Liu, Yue ; Wu, Geng-Feng ; Wang, Wei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-s190t-8107eb98f192cb1b00de087dab4e11e5f37a5f5b2313df4bf374f48fb47163f93</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Feature Selection</topic><topic>Feature Selection Method</topic><topic>Mutual Information</topic><topic>Neural Network</topic><topic>Strong Earthquake</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Tian-Yu</creatorcontrib><creatorcontrib>Li, Guo-Zheng</creatorcontrib><creatorcontrib>Liu, Yue</creatorcontrib><creatorcontrib>Wu, Geng-Feng</creatorcontrib><creatorcontrib>Wang, Wei</creatorcontrib></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Tian-Yu</au><au>Li, Guo-Zheng</au><au>Liu, Yue</au><au>Wu, Geng-Feng</au><au>Wang, Wei</au><au>Zurada, Jacek M.</au><au>Lu, Bao-Liang</au><au>Yi, Zhang</au><au>Yin, Hujun</au><au>Wang, Jun</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Estimation of the Future Earthquake Situation by Using Neural Networks Ensemble</atitle><btitle>Advances in Neural Networks - ISNN 2006</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2006</date><risdate>2006</risdate><spage>1231</spage><epage>1236</epage><pages>1231-1236</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540344827</isbn><isbn>3540344829</isbn><eisbn>9783540344834</eisbn><eisbn>3540344837</eisbn><abstract>Earthquakes will do great harms to the people, to estimate the future earthquake situation in Chinese mainland is still an open issue. There have been previous attempts to solve this problem by using artificial neural networks. In this paper, a novel algorithm named MIFEB is proposed to improve the estimation accuracy by combing bagging of neural networks with mutual information based feature selection for its individuals. MIFEB is compared with the general case of bagging on UCI data sets, then, MIFEB is used to forecast the seismicity of strong earthquakes in Chinese mainland, computation results show that MIFEB obtains higher accuracy than other several methods like bagging of neural networks and single neural networks do.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11760191_179</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Neural Networks - ISNN 2006, 2006, p.1231-1236 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_springer_books_10_1007_11760191_179 |
| source | Springer Books |
| subjects | Feature Selection Feature Selection Method Mutual Information Neural Network Strong Earthquake |
| title | Estimation of the Future Earthquake Situation by Using Neural Networks Ensemble |
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