A principled approach for building and evaluating neural network classification models
In this paper, we propose a principled approach to building and evaluating neural network classification models for decision support system (DSS) implementations. First, the usefulness of neural networks for use with e-commerce data and for Bayesian classification is discussed. Next, the theory conc...
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| Veröffentlicht in: | Decision Support Systems 2004-11, Vol.38 (2), p.233-246 |
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| container_title | Decision Support Systems |
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| creator | Berardi, Victor L. Patuwo, B.Eddy Hu, Michael Y. |
| description | In this paper, we propose a principled approach to building and evaluating neural network classification models for decision support system (DSS) implementations. First, the usefulness of neural networks for use with e-commerce data and for Bayesian classification is discussed. Next, the theory concerning model accuracy and generalization is presented. Then, the principled approach, which is developed with consideration of these issues, is described. Through an illustrative problem, it is seen that when problem complexity is considered, the classification performance of the neural networks can be much better than what is observed. Furthermore, it is seen that model order selection processes based upon a single dataset can lead to an incorrect conclusion concerning the best model, which impacts model error and utility. |
| doi_str_mv | 10.1016/S0167-9236(03)00093-9 |
| format | Article |
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Furthermore, it is seen that model order selection processes based upon a single dataset can lead to an incorrect conclusion concerning the best model, which impacts model error and utility.</description><subject>Artificial neural networks</subject><subject>Classification</subject><subject>Computer based modeling</subject><subject>Data utilization</subject><subject>Decision processes</subject><subject>Decision support systems</subject><subject>E-commerce</subject><subject>Mathematical models</subject><subject>Model bias</subject><subject>Model error</subject><subject>Model variance</subject><subject>Neural networks</subject><subject>Studies</subject><subject>Systems development</subject><issn>0167-9236</issn><issn>1873-5797</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><recordid>eNqFUMtOwzAQtBBIlMInIFmc4BBYx2ncnFBV8ZIqceBxtRx7DS5pHOykiL_HbRFXLjta7czuzhByyuCSASuvnlIRWZXz8hz4BQBUPKv2yIhNBc8mohL7ZPRHOSRHMS4BSi6m5Yi8zmgXXKtd16ChquuCV_qdWh9oPbjGuPaNqtZQXKtmUP2mbXEIqknQf_nwQXWjYnTW6TT1LV15g008JgdWNRFPfnFMXm5vnuf32eLx7mE-W2Sa86LPGKhc1WWlmUIoikIXFnEyxdxwzcDoPDfKgDVCFKpGnqypuhK11ViBLrjlY3K225v-_hww9nLph9CmkzKHsgTGRJ5Ikx1JBx9jQCuT5ZUK35KB3CQotwnKTTwSuNwmKKuku97pkiFcOwwyaoetRuMC6l4a7_7Z8AMvp3oV</recordid><startdate>20041101</startdate><enddate>20041101</enddate><creator>Berardi, Victor L.</creator><creator>Patuwo, B.Eddy</creator><creator>Hu, Michael Y.</creator><general>Elsevier B.V</general><general>Elsevier Sequoia S.A</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20041101</creationdate><title>A principled approach for building and evaluating neural network classification models</title><author>Berardi, Victor L. ; Patuwo, B.Eddy ; Hu, Michael Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c334t-10a2ab69c1ae0444c4fee58e2d3c10dc22dad0fd774abe3000ab97bfce90c43f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Artificial neural networks</topic><topic>Classification</topic><topic>Computer based modeling</topic><topic>Data utilization</topic><topic>Decision processes</topic><topic>Decision support systems</topic><topic>E-commerce</topic><topic>Mathematical models</topic><topic>Model bias</topic><topic>Model error</topic><topic>Model variance</topic><topic>Neural networks</topic><topic>Studies</topic><topic>Systems development</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Berardi, Victor L.</creatorcontrib><creatorcontrib>Patuwo, B.Eddy</creatorcontrib><creatorcontrib>Hu, Michael Y.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Decision Support Systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Berardi, Victor L.</au><au>Patuwo, B.Eddy</au><au>Hu, Michael Y.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A principled approach for building and evaluating neural network classification models</atitle><jtitle>Decision Support Systems</jtitle><date>2004-11-01</date><risdate>2004</risdate><volume>38</volume><issue>2</issue><spage>233</spage><epage>246</epage><pages>233-246</pages><issn>0167-9236</issn><eissn>1873-5797</eissn><coden>DSSYDK</coden><abstract>In this paper, we propose a principled approach to building and evaluating neural network classification models for decision support system (DSS) implementations. 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| ispartof | Decision Support Systems, 2004-11, Vol.38 (2), p.233-246 |
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| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Artificial neural networks Classification Computer based modeling Data utilization Decision processes Decision support systems E-commerce Mathematical models Model bias Model error Model variance Neural networks Studies Systems development |
| title | A principled approach for building and evaluating neural network classification models |
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