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<meta content="This paper investigates the use, for the task of classifier learning in the presence of misclassification costs, of some gradient descent style leveraging approaches to classifier learning: Schapire and Singer's AdaBoost.MH and AdaBoost.MR [16], and Collins et al's multi-class logistic regression method [4], and some modifications that retain the gradient descent style approach. Decision trees and stumps are used as the underlying base classifiers, learned from modified versions of Quinlan's C4.5 [15]. Experiments are reported comparing the performance, in terms of average cost, of the modified methods to that of the originals, and to the previously suggested "Cost Boosting" methods of Ting and Zheng [21] and Ting [18], which also use decision trees based upon modified C4.5 code, but do not have an interpretation in the gradient descent framework. While some of the modifications improve upon the originals in terms of cost performance for both trees and stumps, the comparison with tree-based Cost Boosting suggests that out of the methods first experimented with here, it is one based on stumps that has the most promise.
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<h1 class="ep_tm_pagetitle">Gradient Descent Style Leveraging of Decision Trees and Stumps for Misclassification Cost Performance</h1>
<p style="margin-bottom: 1em" class="not_ep_block"><span class="person_name">Cameron-Jones, Mike</span> (2001) <xhtml:em>Gradient Descent Style Leveraging of Decision Trees and Stumps for Misclassification Cost Performance.</xhtml:em> In: AI 2001: Advances in Artificial Intelligence, 14th Australian Joint Conference on Artificial Intelligence, 10-14 Dec 2001, Adelaide, Australia.</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_825' );" href="http://eprints.utas.edu.au/825/1/OzAI2001.pdf" onmouseout="EPJS_HidePreview( event, 'doc_preview_825' );"><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_825"><table><tr><td><img alt="" src="http://eprints.utas.edu.au/825/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/825/1/OzAI2001.pdf"><span class="ep_document_citation">PDF (Author Version)</span></a> - Requires a PDF viewer<br />424Kb</td></tr></table><p style="margin-bottom: 1em" class="not_ep_block">Official URL: <a href="http://www.springerlink.com/content/8wa6n94gm9ufe6dv/">http://www.springerlink.com/content/8wa6n94gm9ufe6dv/</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 use, for the task of classifier learning in the presence of misclassification costs, of some gradient descent style leveraging approaches to classifier learning: Schapire and Singer's AdaBoost.MH and AdaBoost.MR [16], and Collins et al's multi-class logistic regression method [4], and some modifications that retain the gradient descent style approach. Decision trees and stumps are used as the underlying base classifiers, learned from modified versions of Quinlan's C4.5 [15]. Experiments are reported comparing the performance, in terms of average cost, of the modified methods to that of the originals, and to the previously suggested "Cost Boosting" methods of Ting and Zheng [21] and Ting [18], which also use decision trees based upon modified C4.5 code, but do not have an interpretation in the gradient descent framework. While some of the modifications improve upon the originals in terms of cost performance for both trees and stumps, the comparison with tree-based Cost Boosting suggests that out of the methods first experimented with here, it is one based on stumps that has the most promise.
</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">825</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=825;">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=825">item control page</a></p>
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