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<meta content="This paper investigates the application of the multiple classifier technique known as "stacking" [23], to the task of classifier learning for misclassification cost performance, by straightforwardly adapting a technique successfully developed by Ting and Witten [19, 20] for the task of
classifier learning for accuracy performance. Experiments are reported comparing the performance of the stacked classifier with that of its component classifiers, and of other proposed cost-sensitive multiple classifier methods -- a variation of "bagging", and two "boosting" style methods. These experiments confirm that stacking is competitive with the other methods that have previously been proposed. Some further experiments examine the performance of stacking methods with different numbers of component classifiers, including the case of stacking a single classifier, and provide the first demonstration that stacking a single classifier can be beneficial for many data sets.
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classifier learning for accuracy performance. Experiments are reported comparing the performance of the stacked classifier with that of its component classifiers, and of other proposed cost-sensitive multiple classifier methods -- a variation of "bagging", and two "boosting" style methods. These experiments confirm that stacking is competitive with the other methods that have previously been proposed. Some further experiments examine the performance of stacking methods with different numbers of component classifiers, including the case of stacking a single classifier, and provide the first demonstration that stacking a single classifier can be beneficial for many data sets.
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<h1 class="ep_tm_pagetitle">Stacking for Misclassification Cost Performance</h1>
<p style="margin-bottom: 1em" class="not_ep_block"><span class="person_name">Cameron-Jones, Mike</span> and <span class="person_name">Charman-Williams, Andrew</span> (2001) <xhtml:em>Stacking for Misclassification Cost Performance.</xhtml:em> In: Advances in Artificial Intelligence, 14th Biennial Conference of the Canadian AI Society for Computational Studies of Intelligence, 7-9 Jun 2001, Ottawa, Canada.</p><p style="margin-bottom: 1em" class="not_ep_block"></p><table style="margin-bottom: 1em" class="not_ep_block"><tr><td valign="top" style="text-align:center"><a onmouseover="EPJS_ShowPreview( event, 'doc_preview_826' );" href="http://eprints.utas.edu.au/826/1/CanAI2001.pdf" onmouseout="EPJS_HidePreview( event, 'doc_preview_826' );"><img alt="[img]" src="http://eprints.utas.edu.au/style/images/fileicons/application_pdf.png" class="ep_doc_icon" border="0" /></a><div class="ep_preview" id="doc_preview_826"><table><tr><td><img alt="" src="http://eprints.utas.edu.au/826/thumbnails/1/preview.png" class="ep_preview_image" border="0" /><div class="ep_preview_title">Preview</div></td></tr></table></div></td><td valign="top"><a href="http://eprints.utas.edu.au/826/1/CanAI2001.pdf"><span class="ep_document_citation">PDF (Author Version)</span></a> - Requires a PDF viewer<br />376Kb</td></tr></table><p style="margin-bottom: 1em" class="not_ep_block">Official URL: <a href="http://www.springerlink.com/content/xdkq3jpj0gmetre4/">http://www.springerlink.com/content/xdkq3jpj0gmetre4/</a></p><div class="not_ep_block"><h2>Abstract</h2><p style="padding-bottom: 16px; text-align: left; margin: 1em auto 0em auto">This paper investigates the application of the multiple classifier technique known as "stacking" [23], to the task of classifier learning for misclassification cost performance, by straightforwardly adapting a technique successfully developed by Ting and Witten [19, 20] for the task of
classifier learning for accuracy performance. Experiments are reported comparing the performance of the stacked classifier with that of its component classifiers, and of other proposed cost-sensitive multiple classifier methods -- a variation of "bagging", and two "boosting" style methods. These experiments confirm that stacking is competitive with the other methods that have previously been proposed. Some further experiments examine the performance of stacking methods with different numbers of component classifiers, including the case of stacking a single classifier, and provide the first demonstration that stacking a single classifier can be beneficial for many data sets.
</p></div><table style="margin-bottom: 1em" cellpadding="3" class="not_ep_block" border="0"><tr><th valign="top" class="ep_row">Item Type:</th><td valign="top" class="ep_row">Conference or Workshop Item (Paper)</td></tr><tr><th valign="top" class="ep_row">Additional Information:</th><td valign="top" class="ep_row">The original publication is available at www.springerlink.com</td></tr><tr><th valign="top" class="ep_row">Subjects:</th><td valign="top" class="ep_row"><a href="http://eprints.utas.edu.au/view/subjects/280200.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280000.html">280000 Information, Computing and Communication Sciences</a></td></tr><tr><th valign="top" class="ep_row">ID Code:</th><td valign="top" class="ep_row">826</td></tr><tr><th valign="top" class="ep_row">Deposited By:</th><td valign="top" class="ep_row"><span class="ep_name_citation"><span class="person_name">Dr R. Mike Cameron-Jones</span></span></td></tr><tr><th valign="top" class="ep_row">Deposited On:</th><td valign="top" class="ep_row">19 Mar 2007</td></tr><tr><th valign="top" class="ep_row">Last Modified:</th><td valign="top" class="ep_row">09 Jan 2008 02:30</td></tr><tr><th valign="top" class="ep_row">ePrint Statistics:</th><td valign="top" class="ep_row"><a target="ePrintStats" href="/es/index.php?action=show_detail_eprint;id=826;">View statistics for this ePrint</a></td></tr></table><p align="right">Repository Staff Only: <a href="http://eprints.utas.edu.au/cgi/users/home?screen=EPrint::View&eprintid=826">item control page</a></p>
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