Relative-Fuzzy (Paperback)

Relative-Fuzzy is a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. This approach is based on a novel type of fuzzy logic which has been called Relative-Fuzzy Logic (RFL). RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. Two types of logic; namely fuzzy logic and possible-world logic, have been mixed to produce a new membership value set that is able to handle fuzziness and multiple viewpoints at the same time, which called Relative-Fuzzy membership value set. For implementation purpose, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net along with its new learning and recalling algorithms has been developed. This new type of HNN is considered to be a RFL computation based machine.