Data-based creation of diagnostic rules

Abstract

W pracy przedstawiono metodę tworzenia reguł diagnostycznych o rozmytych przesłankach reprezentujących objawy i nierozmytej konkluzji odpowiadającej diagnozie. Reguły tworzy się na podstawie danych uczących, lecz są one zrozumiałe dla ekspertów i mogą być przez nich weryfikowane. Zbiór reguł dla każdej z diagnoz jest ustalany odrębnie, z zastosowaniem oryginalnego algorytmu eliminacji reguł. Obliczenia dla dwóch benchmarkowych baz danych potwierdzają efektywność proponowanych metod.A method of diagnostic rule creation is presented in the paper. The rules have fuzzy premises that represent symptoms and a crisp conclusion relevant to the diagnosis. Each rule has an assigned weight that is determined as a value of the basic probability assignment defined in the Dempster-Shafer theory. Having created the rules, there is performed the diagnostic reasoning for a consulted case whose outcomes are values of the Bel belief measure (of the Dempster-Shafer theory) for all diagnostic hypotheses. The hypothesis of the maximal belief is the ultimate conclusion. Membership functions of symptoms and the basic probability assignment are found from the training data. Although the rules are created by means of data, they are understandable for human experts who can interpret and verified them. An individual set of rules is provided for each diagnosis. It results from an original elimination algorithm that is proposed in the paper. The elimination process starts from the complete set of rules and the algorithm indicates rule(s) of the lowest diagnostic significance, which are next deleted. Numerical experiments for two benchmark databases show the properties of the proposed method