County level of urbanization quality classification based on support vector machine
The county level of urbanization quality analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of urbanization quality data which is large scale and imbalance, this paper present...
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| creator | Zhao Jing Guo Haixing |
| description | The county level of urbanization quality analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of urbanization quality data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of urbanization quality. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding the county level of urbanization quality classification for Guanzhong urban agglomeration. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for county level of urbanization quality classification and prediction. |
| doi_str_mv | 10.1109/ICIII.2013.6703167 |
| format | Conference Proceeding |
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According to the county level of urbanization quality data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of urbanization quality. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding the county level of urbanization quality classification for Guanzhong urban agglomeration. 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According to the county level of urbanization quality data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of urbanization quality. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding the county level of urbanization quality classification for Guanzhong urban agglomeration. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for county level of urbanization quality classification and prediction.</description><subject>Accuracy</subject><subject>Artificial neural networks</subject><subject>classification</subject><subject>county level of urbanization quality</subject><subject>Data models</subject><subject>Guanzhong urban agglomeration</subject><subject>Kernel</subject><subject>Regression tree analysis</subject><subject>support vector machine</subject><subject>Support vector machines</subject><issn>2155-1456</issn><issn>2155-1472</issn><isbn>9781479939855</isbn><isbn>1479939854</isbn><isbn>1479902454</isbn><isbn>9781479902453</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9kMtqwzAURNUXNEnzA-1GP-D0Std6LYvpwxDootkHSZapimOnlh1Iv76mCV3NcA7MYgi5Z7BiDMxjWZRlueLAcCUVIJPqgsxZrowBnov8ksw4EyKbCL8iS6P0n0Ojhbj-d0LeknlKXwASJcKMfBTd2A5H2oRDaGhX07F3to0_dohdS79H28TJ-samFOvoT9jZFCo6lTTu910_0EPwQ9fTnfWfsQ135Ka2TQrLcy7I5uV5U7xl6_fXsnhaZ9HAkJnaC5uLwDia4JzIeV15xoPyYLWuUTOHNgACSsUqXSlvLCIaB04a7RAX5OE0G0MI230fd7Y_bs_f4C8PS1WB</recordid><startdate>201311</startdate><enddate>201311</enddate><creator>Zhao Jing</creator><creator>Guo Haixing</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201311</creationdate><title>County level of urbanization quality classification based on support vector machine</title><author>Zhao Jing ; Guo Haixing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-9fc5a45e1239ebb542fdc12e7c0a88f381b3ae0303671d8d7c9a3339b0b698b33</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Accuracy</topic><topic>Artificial neural networks</topic><topic>classification</topic><topic>county level of urbanization quality</topic><topic>Data models</topic><topic>Guanzhong urban agglomeration</topic><topic>Kernel</topic><topic>Regression tree analysis</topic><topic>support vector machine</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhao Jing</creatorcontrib><creatorcontrib>Guo Haixing</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhao Jing</au><au>Guo Haixing</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>County level of urbanization quality classification based on support vector machine</atitle><btitle>2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering</btitle><stitle>ICIII</stitle><date>2013-11</date><risdate>2013</risdate><volume>2</volume><spage>388</spage><epage>391</epage><pages>388-391</pages><issn>2155-1456</issn><eissn>2155-1472</eissn><isbn>9781479939855</isbn><isbn>1479939854</isbn><eisbn>1479902454</eisbn><eisbn>9781479902453</eisbn><abstract>The county level of urbanization quality analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of urbanization quality data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of urbanization quality. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding the county level of urbanization quality classification for Guanzhong urban agglomeration. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for county level of urbanization quality classification and prediction.</abstract><pub>IEEE</pub><doi>10.1109/ICIII.2013.6703167</doi><tpages>4</tpages></addata></record> |
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| subjects | Accuracy Artificial neural networks classification county level of urbanization quality Data models Guanzhong urban agglomeration Kernel Regression tree analysis support vector machine Support vector machines |
| title | County level of urbanization quality classification based on support vector machine |
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