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Fuzzy Set Approaches

  • Fuzzy logic uses truth values between 0.0 and 1.0 to represent the degree of membership (such as in a fuzzy membership graph)
  • Attribute values are converted to fuzzy values. Ex.:
    • Income, x, is assigned a fuzzy membership value to each of the discrete categories {low, medium, high}, e.g. $49K belongs to “medium income” with fuzzy value 0.15 but belongs to “high income” with fuzzy value 0.96
    • Fuzzy membership values do not have to sum to 1.
  • Each applicable rule contributes a vote for membership in the categories
  • Typically, the truth values for each predicted category are summed, and these sums are combined

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