Comparison of incomplete data handling techniques for neuro-fuzzy system


  • Marcin Sikora Independent researcher
  • Krzysztof Simiński Silesian University of Technology, Faculty of Automatic Control, Electronics and Computer Science



incomplete data, marginalization, imputation, neuro-fuzzy system, ANNBFIS, PDS, IFCM, OCS, NPS


Real-life data sets sometimes miss some values. The incomplete data needs specialized algorithms or preprocessing that allows the use of the algorithms for complete data. The paper presents a comparison of various techniques for handling incomplete data in the neuro-fuzzy system ANNBFIS. The crucial procedure in the creation of a fuzzy model for the neuro-fuzzy system is the partition of the input domain. The most popular approach (also used in the ANNBFIS) is clustering. The analyzed approaches for clustering incomplete data are: preprocessing (marginalization and imputation) and specialized clustering algorithms (PDS, IFCM, OCS, NPS). The objective of our research is the comparison of the preprocessing techniques and specialized clustering algorithms to find the the most-advantageous technique for handling incomplete data with a neuro-fuzzy system. This approach is also the indirect validation of clustering.


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How to Cite

Sikora, M., & Simiński, K. (2014). Comparison of incomplete data handling techniques for neuro-fuzzy system. Computer Science, 15(4), 441.