Abstract
Three software prototypes were programmed as proofs of concept of the technological aspects of the proposed methods for automated MFI in fuzzy data analysis. Master’s students developed two of them, iFCQL and IFC-Filter for Weka. The author developed the inductive fuzzy classification language IFCL. This implementation also allows experiments on the implemented methods for experimental evaluation. Using a meta-induction approach, the designed method was applied for prediction in several real-world datasets in order to analyze characteristics and optimal parameters of the constructed methodology and to compare it to conventional predictive approaches. Classical inductive statistical methods were applied to gain insights about induction by IFC.
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Notes
- 1.
http://diuf.unifr.ch/is/ifc (accessed 11.2010).
- 2.
http://archive.ics.uci.edu/ml/ (accessed 03.2010).
- 3.
- 4.
- 5.
See Query 2 in Appendix A.
- 6.
See Query 3 and data in Table A.3 in Appendix A.
- 7.
See Query 7 in Appendix A.
- 8.
See Query 8 and data in Table A.4 in Appendix A.
- 9.
See Query 4 in Appendix A.
- 10.
See Query 5 and data in Table A.6 in Appendix A.
- 11.
See Query 9 in Appendix A.
- 12.
See Query 10 and data in Table A.5 in Appendix A.
- 13.
See Query 11 in Appendix A.
- 14.
See Query 12 and data in Table A.7 in Appendix A.
- 15.
See Query 14 in Appendix A.
- 16.
- 17.
See Query 15 and data in Table A.8 in Appendix A.
- 18.
See Query 14 in Appendix A.
- 19.
See Query 15 and data in Table A.9 in Appendix A.
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Kaufmann, M. (2014). Prototyping and Evaluation. In: Inductive Fuzzy Classification in Marketing Analytics. Fuzzy Management Methods. Springer, Cham. https://doi.org/10.1007/978-3-319-05861-0_4
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