Applying Fuzzy Hypothesis Testing to Medical Data. Academic Article uri icon

abstract

  • . Classical statistics and many data mining methods rely on "statistical significance" as a sole criterion for evaluating alternative hypotheses. In this paper, we use a novel, fuzzy logic approach to perform hypothesis testing. The method involves four major steps: hypothesis formulation, data selection (sampling), hypothesis testing (data mining), and decision (results). In the hypothesis formulation step, a null hypothesis and set of alternative hypotheses are created using conjunctive antecedents and consequent functions. In the data selection step, a subset D of the set of all data in the database is chosen as a sample set. This sample should contain enough objects to be representative of the data to a certain degree of satisfaction. In the third step, the fuzzy implication is performed for the data in D for each hypothesis and the results are combined using some aggregation function. These results are used in the final step to determine if the null hypothesis should b...

publication date

  • January 1, 1999