@Article{id:Siminski20233WDNFS,
  author   = {Krzysztof Siminski},
  title    = {{3WDNFS} – Three-way decision neuro-fuzzy system for classification},
  journal = {Fuzzy Sets and Systems},
  volume = {466},
  pages = {108432},
  year = {2023},
  issn = {0165-0114},
  doi = {https://doi.org/10.1016/j.fss.2022.10.021},
  url = {https://www.sciencedirect.com/science/article/pii/S0165011422004626},
  author = {Krzysztof Siminski},
  keywords = {Three-way decision, Neuro-fuzzy system, Granular computing, 
              Explainable artificial intelligence, Interpretable models},
  abstract = {The three-way decision approach is an emerging paradigm in the design 
  of tools for data mining and machine learning. It switches from a two-way 
  classification (“negative” and “positive” class) to three decisions: 
  “negative”, “positive”, and non-commitment class. It means that when for 
  some data it is not possible to elaborate a reliable answer they are assigned 
  with a non-commitment class. In the paper we apply this paradigm for a cascade 
  of neuro-fuzzy classifiers. If the first neuro-fuzzy system assigns a data 
  item with a non-commitment class, the next neuro-fuzzy system is run for 
  this data item. For easy items the first system is enough, but for harder 
  ones two or more systems have to be run. Neuro-fuzzy systems elaborate 
  interpretable fuzzy models. The models are composed of fuzzy rules that can 
  be interpreted linguistically by humans. Application of neuro-fuzzy systems 
  results in a cascade of interpretable models. The paper describes algorithms 
  for training a cascade of neuro-fuzzy classifiers and for elaboration 
  of answers. The paper presents results of numerical experiments that show 
  that this technique can elaborate results with lower generalisation error 
  than two-way classifiers. The implementation of the proposed system 
  is available from a github repository.},
}