@article{id:Siminski2015RoughFSS,
  title={Rough subspace neuro-fuzzy system},
  author={Simi{\'n}ski, Krzysztof},
  journal={Fuzzy Sets and Systems},
 volume = {269},
number = {},
pages = {30-46},
year = {2015},
issn = {0165-0114},
doi = {doi:10.1016/j.fss.2014.07.003},
url = {http://www.sciencedirect.com/science/article/pii/S0165011414003108},
abstract = {The missing values can be an important obstacle 
and chalenging problem in data analysis. The paper presents
the neuro-fuzzy system that handles incomplete data. The systems
 is complete: it can extract the fuzzy rule base from both 
 complete and incomplete data and can elaborate answers for 
 complete and incomplete data. The second major feature of 
 the system is the assignment of weights to attributes in 
 fuzzy rules. The weights are assigned locally: each fuzzy rule
 has its own weights for attributes. This feature may improve 
 the precision of answers elaborated by the system and may
 reveal relations between attributes in the data set. The paper 
 is accompanied by experimental results. The results show that 
 the subspace technique is advantageous in handling data set 
 with missing values. The results also reveal that for 
 approximation of complete data it is better to apply techniques 
 without subspace approach.},
note = {[40 pkt]},
}