@Article{id:Sikora2014Comparison,
  Title                    = {Comparison of Incomplete Data Handling Techniques for Neuro-Fuzzy Systems},
  Author                   = {Marcin Sikora and Krzysztof Simi{\'{n}}ski},
  Journal                  = {Computer Science},
  Year                     = {2014},
  Note                     = {[8 pkt.]},
  Number                   = {4},
  Pages                    = {441-458},
  Volume                   = {15},

  Abstract                 = {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.},
  Doi                      = {dx.doi.org/10.7494/csci.2014.15.4.441}
}