@InProceedings{NavaraPeri:IPMU04,
  IS = { zkontrolovano 13 Jan 2005 },
  UPDATE  = { 2004-11-27 },
  author =      {Navara, Mirko and Peri, Daniele},
  title =       {Automatic generation of fuzzy rules and its applications in medical diagnosis},
  year =        {2004},
  pages =       {657--663},
  booktitle =   {Proceedings of the 10th International Conference on Information Processing and 
                 Management of Uncertainty},
  publisher =   {Universita La Sapienza},
  address =     {Rome, Italy},
  isbn =        {88-87242-54-2},
  book_pages =  {1027},
  month =       {July},
  day =         {4-9},
  venue =       {Perugia, Italy},
  organization ={Universita La Sapienza},
  annote = {Fuzzy Rule Learner (FURL) is a theory revision approach to
    fuzzy rules learning based on Hierarchical Prioritized
    Structures. Each new level is composed from exceptions to rules
    from the preceding levels. The new rules are chosen in order to
    eliminate the biggest classification errors found in the training
    data. FURL may me combined with many techniques used to interpret
    rule bases in fuzzy controllers.  In the traditional approaches to
    fuzzy approximation, the learning of rules has an undesirable
    effect. When many new rules are added, the interpretation of the
    rule base tends to one of its extreme values, thus we loose its
    informational value.  In this paper, we suggest and test two
    methods which may overcome this drawback, negated antecedents and
    a controller with conditionally firing rules.  We show that they
    allow to improve the performance of systems based on learning of
    fuzzy rules, namely the Fuzzy Rule Learner.  The methods are
    tested on ECG and Multiple Sclerosis Disease datasets.  },
  keywords =    {Fuzzy Rule Learner, Hierarchical Prioritized Structure, 
                 Mamdani--Assilian controller, controller with conditionally firing rules, 
                 negated antecedent, medical diagnosis, classification},
  prestige =    {international},
  importance =  {1},
  authorship =  {50-50},
  project =     {GACR 201/02/1540},
}