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Selected Projects

SFB 475 - Project A4

  • Duration: since 07/1997 (DFG)
  • Project Leader: Prof. Dr. Katharina Morik, Prof. Dr. Claus Weihs
  • Staff: Thorsten Joachims, Stefan Rüping, Ralf Klinkenberg, Ingo Mierswa, Martin Scholz, Michael Wurst
  • URL: SFB 475 - A4

The aim of project A4 is to combine statistical methods and methods of machine learning in order to improve Knowledge Discovery in Databases (KDD). After the process of the knowledge discovery was examined as a whole in the last period, we focus on two important problems in the current period. These problems often occur in practice of knowledge discovery. Corresponding research promises a special synergy effect because of the combination of statistical methods and machine learning methods: analysis temporal phenomenons in the form of events and the application of experimental design. Additionally, emphasis of the project is placed on the applied analysis of real databases.

Selected Publications

Mierswa, Ingo and Morik, Katharina. Automatic Feature Extraction for Classifying Audio Data. Machine Learning Journal, 58, 127-149, 2005. [pdf]
Mierswa, Ingo and Wurst, Michael. Efficient Case Based Feature Construction for Heterogeneous Learning Tasks. In Proceedings of the European Conference on Machine Learning (ECML), Springer-Verlag, Berlin, 641-648, 2005. [pdf]
Morik, Katharina and Siebes, Arno and Boulicault, Jean-François (editors). Detecting Local Patterns, Springer Lecture Notes in Artificial Intelligence, Volume 3539, Springer-Verlag, Berlin, 2005. Springer
Rüping, Stefan and Scheffer, Tobias (editors). Proceedings of the ICML 2005 Workshop on Learning with Multiple Views, 2005.
Scholz, Martin. Sampling-Based Sequential Subgroup Mining. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Databases (KDD), 265-274, 2005.
Klinkenberg, Ralf and Rüping, Stefan. Concept Drift and the Importance of Examples. In Franke, Jürgen and Nakhaeizadeh, Gholamreza and Renz, Ingrid (editors), Text Mining - Theoretical Aspects and Applications, Seiten 55--77, Physica-Verlag, Berlin, 2003.
Morik, Katharina and Rüping, Stefan. A Multistrategy Approach to the Classification of Phases in Business Cycles. In Proceedings of the European Conference on Machine Learning (ECML), Springer-Verlag, 307-318, 2002. [pdf]
Joachims, Thorsten. Estimating the Generalization Performance of a SVM Efficiently. In Proceedings of the International Conference on Machine Learning (ICML), Morgan Kaufman, 431-438, 2000. [pdf]
Joachims, Thorsten. Making large-Scale SVM Learning Practical. In: Advances in Kernel Methods - Support Vector Learning. MIT Press, 1999. [pdf]
Joachims, Thorsten. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning (ECML), Springer-Verlag, 137-142, 1998. [pdf]

KDUbiq

  • Duration: ab 01/2006 (EU)
  • Project Leader: Fraunhofer Institut for Intelligent Autonomous Systems
  • Staff: Katharina Morik, Sebastian Land
  • URL:http://www.kdubiq.org

KDUbiq brings together newly emerging research in ubiquitous knowledge discovery. This multi-disciplinary approach constitutes a paradigm shift for the field of knowledge discovery since the idea of standalone analysis tools is abandoned in favour of process integrated, distributed and autonomous analysis systems.

Selected Publications

SFB 531 - Project B5

  • Duration: 01/2000 - 12/2002 (DFG)
  • Project Leader: Prof. Dr. Katharina Morik
  • Staff: Oliver Ritthoff, Ralf Klinkenberg, Ingo Mierswa
  • URL: SFB 531 - B5

The goal of this project is the identification and formalization of practically relevant learning tasks on the basis of applications in the C-projects. Particular learning tasks which deviate from the standard scenario of classification respectively optimization as, e.g., learning with non-factual knowledge, repeated learning of similar concepts, learning of temporally varying concepts and feature selection/construction will be considered. In this context the problem of feature selection/construction will be a central aspect in the scope of investigations.

Selected Publications

Klinkenberg, Ralf. Learning Drifting Concepts: Example Selection vs. Example Weighting. In Intelligent Data Analysis (IDA), Special Issue on Incremental Learning Systems Capable of Dealing with Concept Drift, Vol. 8, No. 3, 2004.
Klinkenberg, Ralf and Rüping, Stefan. Concept Drift and the Importance of Examples. In Franke, Jürgen and Nakhaeizadeh, Gholamreza and Renz, Ingrid (editors), Text Mining -- Theoretical Aspects and Applications, Seiten 55-77, Berlin, Germany, Physica-Verlag, 2003.
Ritthoff, Oliver and Klinkenberg, Ralf. Evolutionary Feature Space Transformation using Type-Restricted Generators. In Cantu-Paz, E. et al.(editors), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2003) - Part II, Seiten 1606-1607, Springer, 2003.
Ritthoff, Oliver and Klinkenberg, Ralf and Fischer, Simon and Mierswa, Ingo. A Hybrid Approach to Feature Selection and Generation Using an Evolutionary Algorithm. In Bullinaria, John A. (editors), Proceedings of the 2002 U.K. Workshop on Computational Intelligence (UKCI-02), Seiten 147-154, Birmingham, UK, University of Birmingham, 2002.
Klinkenberg, Ralf und Joachims, Thorsten. Detecting concept drift with support vector machines. In P. Langley (Hrsg.), Proceedings of the Seventeenth International Conference on Machine Learning (ICML), Seiten 487-494. Morgan Kaufmann, San Francisco, CA, USA, 2000.

SFB 531 - Project C11

  • Duration: 01/2003 - 12/2005 (DFG)
  • Project Leader: Prof. Dr. Katharina Morik, Prof. Dr. Henner Schmidt-Traub
  • Staff: Dipl.-Ing. Bernd Hicking, Dipl.-Inform. Hanna Köpcke, Dipl.-Inform. Ingo Mierswa, Dipl.-Inform. Oliver Ritthoff
  • URL: SFB 531 - C11

The goal of this project is to find optimal positionings for given chemical equipment with methods from the field of Computational Intelligence. We compare and evaluate several knowledge-based and numerical approaches to optimize a plant layout under given constraints. Up to now previous knowledge is not used for sub-symbolic optimization and ideas of knowledge-based optimization should be transferred into Computation Intelligence. This knowledge is extracted from plans provided by engineers.

Selected Publications

Morik, Katharina and Schmidt-Traub, Henner and Hicking, Bernd and Köpcke, Hanna and Mierswa, Ingo. Layout optimization for chemical plants. In Industriemanagement, 2005.
Mierswa, Ingo. Incorporating Fuzzy Knowledge into Fitness: Multiobjective Evolutionary 3D Design of Process Plants. In Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2005, Washington D.C., USA, 2005.

AWAKE

  • Duration: 04/2001 - 12/2003 (BMBF)
  • Project Leader: Fraunhofer for Media Communication
  • Staff: Michael Wurst, Katharina Morik
  • URL: http://awake.imk.fhg.de

The aim of the project Awake is to explore how implicit knowledge structures in different communities of experts can be discovered, visualised and employed for semantic navigation of information spaces and construction of new knowledge. The developed methods combine semantic text analysis with Machine Learning and interfaces for visualising relationships and creating new knowledge structures. Application scenarios include automatic generation of personalised knowledge portals, collaborative semantic exploration of complex information spaces and construction of shared ontology networks for the SemanticWeb. The real-world testbed and context of development is the Internet platform netzspannung.org that aims at establishing a knowledge portal connecting digital art, culture and information technology.

Selected Publications

Novak, Jasminko and Wurst, Michael. Supporting Knowledge Creation and Sharing in Communities Based on Mapping Implicit Knowledge. In j-jucs, Vol. 10, No. 3, pages 235--251, 2004.
Wurst, Michael and Novak, Jasminko. Knowledge Sharing im Heterogeneous Expert Communities based on Personal Taxonomies. In ECAI Workshop on Agent Mediated Knowledge Management, 2004.
Novak, Jasminko and Wurst, Michael. Discovering, Visualizing and Sharing Knowledge through Personalized Learning Knowledge Maps. In Agent Mediated Knowledge Management, 2003.
Novak, Jasminko and Wurst, Michael. Supporting Communities of Practice Through Personalisation and Collaborative Structuring based on Capturing Implicit Knowledge. In Proceedings of the International Conference on Knowledge Management, 2003.
Morik, Katharina and Wurst, Michael. Knowledge Dicovery and Knowledge Visualization, Perspektiven vernetzter Wissensraeume, Workshop 2002. 2002.

Mining Mart

  • Duration: 01/2000 - 02/2003 (EU)
  • Project Leader: Katharina Morik
  • Staff: Katharina Morik, Martin Scholz, Timm Euler, Harald Liedtke
  • URL:http://mmart.cs.uni-dortmund.de

Within the data mining process considerable time is spent for pre-processing the data. Practical experiences have shown that the time spent on preprocessing can take from 50% up to 80% of the entire data mining process when using the traditional attribute-value learners. Thats why preprocessing is the key issue in data analysis. The time is spend for:

  • Choosing the learning task
  • Sampling
  • Feature generation, extraction, and selection
  • Data cleaning
  • Model selection or tuning the hypothesis space
  • Defining appropriate evaluation criteria

Experienced users can apply any learning system successfully to any application, since they prepare the data well. The representation of examples and the choice of a sample determines the applicability of learning methods. A chain of data transformations (learning steps or manual preprocessing) delivers the desired result. Experienced users remember prototypical successful transformation/learning chains.

Selected Publications

Euler, Timm. Publishing Operational Models of Data Mining Case Studies. In Proceedings of the Workshop on Data Mining Case Studies at the 5th IEEE International Conference on Data Mining (ICDM), pages 99--106, Houston, Texas, USA, 2005.
Euler, Timm. Modelling Data Mining Processes on a Conceptual Level. In Proceedings of the 5th International Conference on Decision Support for Telecommunications and Information Society, Warsaw, Poland, 2005.
Morik, Katharina and Scholz, Martin. The MiningMart Approach to Knowledge Discovery in Databases. In Ning Zhong and Jiming Liu (editors), Intelligent Technologies for Information Analysis, pages 47--65, Springer, 2004.
Kietz, Jörg-Uwe and Vaduva, Anca and Zücker, Regina, MiningMart: Metadata-Driven Preprocessing. In Proceedings of the ECML/PKDD Workshop on Database Support for KDD, 2001.
Kietz, Jörg-Uwe and Vaduva, Anca and Zücker, Regina, Mining Mart: Combining Case-Based-Reasoning and Multi-Strategy Learning into a Framework to reuse KDD-Application. In Proceedings of the 5th International Workshop on Multistrategy Learning, R.S. Michalki and P. Brazdil (editors), 2000.
Morik, Katharina. The Representation Race - Preprocessing for Handling Time Phenomena. In Proceedings of the European Conference on Machine Learning, Barcelona, Spain, Springer, 2000.

COMRIS

The COMRIS project aims to develop, demonstrate and experimentally evaluate a scalable approach to integrating the Inhabited Information Spaces schema with a concept of software agents. The COMRIS vision of co-habited mixed-reality information spaces emphasizes the co-habitation of software and human agents in a pair of closely coupled spaces, a virtual and a real one. However, this project does not pursue the perceptual integration of real and virtual space into an augmented reality. Instead the coupling aims at focusing the large potential for useful social interactions in each of the spaces, so that they become more manageable, goal-directed and effective.

Selected Publications

Cranefield, Stephen and Haustein, Stefan and Purvis, Martin. UML-Based Ontology Modelling for Software Agents. In Proceedings of the Autonomous Agents 2001 Workshop on Ontologies in Agent Systems, 2001.
Haustein, Stefan. Semantic Web Languages: RDF vs. SOAP Serialization. In Proceedings of the Second International Workshop on the Semantic Web at WWW10, 2001.
Haustein, Stefan. Utilising an Ontology Based Repository to Connect Web Miners and Application Agents. In Proceedings of the ECML/PKDD Workshop on Semantic Web Mining, 2001.
Haustein, Stefan and Lüdecke, Sascha and Schwering, Christian. The Knowledge Agency. In Proceedings of the Forth International Conference on Autonomous Agents, pages 205 -- 206, ACM SIGART, Barcelona, Spain, ACM Press, New York, 2000.
Haustein, Stefan and Lüdecke, Sascha. Towards Information Agent Interoperability. In Cooperative Information Agents IV -- The Future of Information Agents in Cyberspace, Vol. 1860, pages 208 -- 219, Boston, USA, Springer, 2000.
Morik, Katharina and Haustein, Stefan. The Challenge of Discovering Meta--Data. In Proceedings of the Seventeenth National Conference on Artificial Intelligence, American Association for Artificial Intelligence (AAAI), AAAI press, 2000.

BLearn

  • Duration: 9/1992 - 8/1995 (EU)
  • Project Leader: University of Karlsruhe
  • Staff: Volker Klingspor, Katharina Morik, Anke Rieger
  • URL:

Within the project BLearn II machine learning methods are applied to robotics, in order to reduce the time for setting up and modifying robot applications, and in order to make the operation of robots more user-friendly. The task of chair VIII within this project is to integrate logic-based learning into navigation. The goal is to allow a human user to give abstract commands, such as &qoute;Pass through the doorway, turn left and stop &qoute;. In order to execute these commands, the robot has to be able to recognize, for example, a door or a cupboard. In addition, the robot has to be able to find a door and to execute a left turn in a flexible way, adjusting itself to the different spatial conditions. A hierarchy of learning steps has been developed, which starts from sensor data and robot moves, and which leads to operational concepts. They integrate information about perceptions and actions, such that object recognition and action are coupled directly.

Selected Publications

Morik, Katharina and Klingspor, Volker and Kaiser, Michael (editors). Making Robots Smarter -- Combining Sensing and Action through Robot Learning. Kluwer Academic Press, 1999.
Klingspor, Volker and Morik, Katharina and Rieger, Anke. Learning Concepts from Sensor Data of a Mobile Robot. In Machine Learning, Vol. 23, No. 2/3, pages 305-332, 1996.
Klingspor, Volker and Demiris, J. and Kaiser, Michael. Human-Robot-Communication and Machine Learning. In Applied Artificial Intelligence, Vol. 11, No. 7/8, pages 719--746, 1997.
Klingspor, Volker and Morik, Katharina. Towards Concept Formation Grounded on Perception and Action of a Mobile Robot. In U. Rembold and R. Dillmann and L.O. Hertzberger and T. Kanade (editors), IAS--4, Proc. of the 4th Intern. Conference on Intelligent Autonomous Systems, pages 271--278, Amsterdam, IOS Press, 1995.