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</o:shapelayout></xml><![endif]--></head><body lang=PL link=blue vlink=purple><div class=WordSection1><h3><span lang=EN-US style='font-family:"Calibri","sans-serif"'>Call for papers: Special session: Data mining with meta-learning and hierarchical architectures<o:p></o:p></span></h3><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><b><span lang=EN-US style='font-size:13.5pt;mso-fareast-language:PL'>The IEEE World Congress on Computational Intelligence, Beijing, China, 6-11 July 2014<o:p></o:p></span></b></p><p class=MsoNormal><b><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Goals of this special session:</span></b><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'><o:p></o:p></span></p><p class=MsoNormal style='margin-left:36.0pt'><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>This session will be devoted to approaches that <b>in an intelligent way integrate various components of learning algorithms</b> used for data mining, especially in meta-learning, multimodel architectures, facilitating knowledge transfer and deep learning. Integration of machine learning algorithms becomes increasingly more important, especially in applications to hard problems which still wait to be solved, where application of specialized methods that do not use additional knowledge has led to limited success. Hard problems and big data need much more than single neural network or single learning machine. Sophisticated data transformations play more and more important role. Data mining packages contain hundreds of algorithms that may be composed in millions of ways. Automatization of this process requires analysis of learning algorithms at the meta-level. Methods that extract various forms of useful knowledge, share and integrate it for intelligent information processing, are necessary to solve hard problems. Such methods may be inspired by the organization of the brain, or may be based on formal algorithms. One promising direction is to use methods that construct new features, learning from successes of different algorithms, extracting knowledge from indirect, partial learning and using it to build final potential solutions. Another interesting aspect in the construction of complex computational intelligence methods is dealing with different levels of abstraction; useful meta-knowledge may come in the form of highly abstract heuristic knowledge directing the search process for the optimal model, or may be hidden in the details of algorithm implementation.<o:p></o:p></span></p><p class=MsoNormal><b><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>The main subjects of interest are:</span></b><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'><o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Meta-learning algorithms and system architectures.<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Meta-knowledge representation, acquisition, application, re-use and construction, analysis of the usefulness of knowledge.<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Knowledge transfer, knowledge sharing, transfer learning.<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Meta-learning for big data.<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Multimodel architectures, integration of hierarchy of individual models for data mining.<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Multimodel data mining systems/algorithms, which integrate several methods of data analysis at different levels of granularity.<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Data mining that use hybrid/heterogenous models<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Advanced architectures of data mining systems. Combinations of machine learning, neural networks, fuzzy systems, etc.<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Transformation-base learning, including deep learning algorithms.<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Extraction and construction of new features that simplify the complex learning process, including pre-processing methods, multimodal signal processing, extraction of information from specific types of data.<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Methods of reasoning for automatic creation of decision models, estimation of usefulness of knowledge for a given problem.<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:72.0pt;text-indent:-18.0pt;mso-list:l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-size:10.0pt;font-family:Symbol;color:black;mso-fareast-language:PL'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Applications to challenging problems, methods for testing complex systems.<o:p></o:p></span></p><p class=MsoNormal style='margin-left:36.0pt'><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>The session is not strictly limited to the above subjects. Every aspects of meta-learning or other integration of learning algorithms and knowledge are welcome.<o:p></o:p></span></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-align:justify'><b><span style='font-size:13.5pt'>IMPORTANT DATES</span></b><span style='font-size:13.5pt'><o:p></o:p></span></p><table class=MsoNormalTable border=0 cellspacing=10 cellpadding=0><tr><td width=147 style='width:110.4pt;padding:.75pt .75pt .75pt .75pt'><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:9.0pt'><span style='font-size:13.5pt'>December 20, 2013:<o:p></o:p></span></p></td><td style='padding:.75pt .75pt .75pt .75pt'><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:9.0pt'><span style='font-size:13.5pt'>Paper submission deadline.<o:p></o:p></span></p></td></tr><tr><td width=147 style='width:110.4pt;padding:.75pt .75pt .75pt .75pt'><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:9.0pt'><span style='font-size:13.5pt'>March 15, 2014:<o:p></o:p></span></p></td><td style='padding:.75pt .75pt .75pt .75pt'><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:9.0pt'><span style='font-size:13.5pt'>Notification of paper acceptance.<o:p></o:p></span></p></td></tr><tr><td width=147 style='width:110.4pt;padding:.75pt .75pt .75pt .75pt'><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:9.0pt'><span style='font-size:13.5pt'>April 15, 2014:<o:p></o:p></span></p></td><td style='padding:.75pt .75pt .75pt .75pt'><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;margin-left:9.0pt'><span style='font-size:13.5pt'>Final manuscript submission deadline.<o:p></o:p></span></p></td></tr></table><p class=MsoNormal><b><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'><o:p> </o:p></span></b></p><p class=MsoNormal><b><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Organizers:</span></b><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'><o:p></o:p></span></p><p class=MsoNormal style='margin-left:36.0pt'><span lang=EN-US style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Norbert Jankowski and Wlodzislaw Duch<br>Department of Informatics, Nicolaus Copernicus University<br>ul. </span><span style='font-size:13.5pt;color:black;mso-fareast-language:PL'>Grudziadzka 5, 87-100 Toruń, Poland<br>{norbert,wduch} @ is.umk.pl<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p></div></body></html>