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    <meta content="Kim, Yang Sok" name="eprints.creators_name" />
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<meta content="Real-world document classification is an open-ended problem, rather than a close-ended problem, because the document classification domain continually evolves as the time passes. Unlike the close-ended document classification, the participants in the open-ended problem actively take part in the problem solving process. For this reason, it is important to understand the problem solver's behavioral characteristics. This paper proposes a thorough analysis of them. We found that the problem solving strategies are significantly different among participants because of individual differences in cognition among participants" name="eprints.abstract" />
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<meta content="1. Apte, C., F. Damerau, and S.M. Weiss, Automated learning of decision rules for text categorization. ACM Transactions on Information Systems (TOIS), 1994. 12(3): p. 233 - 251.
2. Hirsch, L., M. Saeedi, and R. Hirsch. Evolving Rules for Document Classification. in 8th European Conference, EuroGP 2005. 2005. Lausanne, Switzerland.
3. Goel, V. Comparison of Well-Structured &amp; Ill-Structured Task Environments and Problem Spaces. in Fourteenth Annual Conference of the Cognitive Science Society. 1992. Hillsdale, NJ: Erlbaum.
4. Cook, J., Bridging the Gap Between Empirical Data on Open-Ended Tutorial Interactions and Computational Models. International Journal of Artificial Intelligence in Education, 2001. 12: p. 85-99.
5. Andriessen, J. and J. Sandberg, Where is Education Heading and How About AI? International Journal of Artificial Intelligence in Education, 1999. 10: p. 130-150.
6. Shaw, M.L.G. and J.B. Woodward, Modeling expert knowledge. Knowledge Acquisition, 1990. 2(3): p. 179 - 206.
7. Dillon, R.F. and R.R. Schmeck, Individual Differences in Cognition. Vol. 1. 1983, New York, USA: Academic Press, Inc.
8. Hong, N.S., The Relationship Between Well-Structured and Ill-Structured Problem Solving in Multimedia Simulation, in The Graduate School, College of Education. 1998, The Pennsylvania State University.
9. Byeong Ho, K., Validating Knowledge Acquisition: Multiple Classification Ripple Down Rules, in School of Computer Science and Engineering. 1995, University of New South Wales.
10. Kang, B., P. Compton, and P. Preston. Multiple Classification Ripple Down Rules : Evaluation and Possibilities. in 9th AAAI-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop. 1995. Banff, Canada, University of Calgary.
11. Park, S.S., Y.S. Kim, and B.H. Kang. Web Document Classification: Managing Context Change. in IADIS International Conference WWW/Internet 2004. 2004. Madrid, Spain.
12. Kim, Y.S., et al. Adaptive Web Document Classification with MCRDR. in International Conference on Information Technology: Coding and Computing ITCC 2004. 2004. Orleans,
Las Vegas, Nevada, USA.
13. Compton, P. and D. Richards, Generalising ripple-down rules. Knowledge Engineering and Knowledge Management Methods, Models, and Tools. 12th International Conference,
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    <h1 class="ep_tm_pagetitle">User Behavior Analysis of the Open-Ended Document Classification System</h1>
    <p style="margin-bottom: 1em" class="not_ep_block"><span class="person_name">Kim, Yang Sok</span> and <span class="person_name">Kang, Byeong Ho</span> and <span class="person_name">Choi, Young Ju</span> and <span class="person_name">Park, Sung Sik</span> and <span class="person_name">Park, Gil Cheol</span> and <span class="person_name">Kim, Seok Soo</span> (2006) <xhtml:em>User Behavior Analysis of the Open-Ended Document Classification System.</xhtml:em> In: The 19th Australian Joint Conference on Artificial Intelligence, 4-8 Dec 2006, Hobart, 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_734' );" href="http://eprints.utas.edu.au/724/1/AI06_User_Behavior.pdf" onmouseout="EPJS_HidePreview( event, 'doc_preview_734' );"><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_734"><table><tr><td><img alt="" src="http://eprints.utas.edu.au/724/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/724/1/AI06_User_Behavior.pdf"><span class="ep_document_citation">PDF</span></a> - Requires a PDF viewer<br />186Kb</td></tr></table><div class="not_ep_block"><h2>Abstract</h2><p style="padding-bottom: 16px; text-align: left; margin: 1em auto 0em auto">Real-world document classification is an open-ended problem, rather than a close-ended problem, because the document classification domain continually evolves as the time passes. Unlike the close-ended document classification, the participants in the open-ended problem actively take part in the problem solving process. For this reason, it is important to understand the problem solver's behavioral characteristics. This paper proposes a thorough analysis of them. We found that the problem solving strategies are significantly different among participants because of individual differences in cognition among participants</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">Keywords:</th><td valign="top" class="ep_row">Web Monitoring
MCRDR
Text Classification
Information Retrieval</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/280201.html">280000 Information, Computing and Communication Sciences &gt; 280200 Artificial Intelligence and Signal and Image Processing &gt; 280201 Expert Systems</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280213.html">280000 Information, Computing and Communication Sciences &gt; 280200 Artificial Intelligence and Signal and Image Processing &gt; 280213 Other Artificial Intelligence</a></td></tr><tr><th valign="top" class="ep_row">ID Code:</th><td valign="top" class="ep_row">724</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 Byeong Ho Kang</span></span></td></tr><tr><th valign="top" class="ep_row">Deposited On:</th><td valign="top" class="ep_row">08 Feb 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=724;">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&amp;eprintid=724">item control page</a></p>
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