department of informatics

Inductive Fuzzy Classification (IFC)

Description: 
An inductive fuzzy class IFC regarding a predition target y is a fuzzy set of individuals i from a universe of discourse U defined by the fuzzy restriction i is likely a member of y. There are several ways to induce a membership function to IFC(y). In the mentioned publications, it is proposed to calcluate normalizations of likelihood comparisons.

(IFC Overview.pdf)

The membership functions (MF) induced accordingly, can be used for data selection, visualization, and prediction in analytics. Correlations of MF with binary target indicators rank attributes by their relevance. 2D graphs of induced MF's provide an intuitive view into an attribute's target association. And finally, an inductive fuzzification of attributes using the induced MF's can improve predicitve performance of existing prediction algorithms.

IFC-Filter for Weka

A Weka-implementation of the IFC-NLR machine learning algorithm can be downloaded at the bottom of this page. A user manual and the source code are available

Inductive Fuzzy Grassroots Ontologies (IFGO) Prototype

A proof of concept of the IFGO semantics extraction algorithm can be evaluated here.

IFCL prototype

The IFCL prototype can be made available, please contact Michael Kaufmann by e-Mail (it is 80MB).

Publications

  • Kaufmann, M., Portmann E., & Fathi M. (2013).  A Concept of Semantics Extraction from Web Data by Induction of Fuzzy Ontologies. IEEE International Conference on Electro/Information Technology. 
  • Portmann, E., Kaufmann M., & Graf C. (2012).  A Distributed, Semiotic-Inductive, and Human-Oriented Approach to Web-Scale Knowledge Retrieval.  The 2012 International Workshop on Web-scale Knowledge Representation, Retrieval, and Reasoning (Web-KR 2012), Maui Hawaii, USA. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012) . 
  • Kaufmann, M. (2012).  Inductive Fuzzy Classification in Marketing Analytics. PhD Thesis, University of Fribourg, 2012.
  • Kaufmann M., Graf C. (2012): Inductive Fuzzy Classification for Analytic CRM. In: Meier A. & Donzé L. (Eds.), Handbook of Research in Fuzzy Methods for Marketing and CRM (2012) IGI Global Link
  • Graf, C. (2010). Erweiterung des Data-Mining-Softwarepakets WEKA um induktive
    unscharfe Klassifikation (Master's Thesis). Department of Informatics, University of
    Fribourg, Switzerland.
  • Kaufmann M. (2009): An Inductive Approach to Fuzzy Marketing Analytics. The 13th IASTED International Conference on Artificial Intelligence and Soft Computing, September 2009, Palma de Mallorca, Spain, pp. 107 - 112. 
  • Zumstein, D., & Kaufmann M. (2009).  A Fuzzy Web Analytics Model for Web Mining. (Ajith P. Abraham, Ed.).Proceedings of the IADIS Multi Conference on Computer Science and Information Systems (MCCSIS 2009) on Data Mining. 59-66 Download Proceedings
  • Kaufmann M., Meier A. (2009): An Inductive Fuzzy Classification Approach applied to Individual Marketing. The 28th North American Fuzzy Information Processing Society Annual Conference, June 2009, Cincinnati Ohio, USA 
    IEEE Link
  • Kaufmann, M., Meier, A. (2008): An Inductive Fuzzy Classification Approach applied to Individual Marketing, Internal Working Paper, University of Fribourg, 2008.
    Download Paper