Abstract

In this paper, we introduce a new more powerful hybrid algorithm which integrates the advantages of rough set theory and fuzzy set theory together with Genetic Algorithms (GAs). Our Genetic-Based Fuzzy Rough Decision Model algorithm consists of four phases: (1) automatic attributes fuzzification, (2) Eliminate redundant attributes using rough set theory, (3) Generating Fuzzy rough rules then calculate automatically fitness value (Confidence) and support for each rule, (4) Using the genetic algorithm for the Fuzzy rough rules. In phase one, the user input the number of fuzzy sets of each attributes, our algorithm determine the maximum and minimum values of each attribute that define and calculates automatically the width (Δ) which divides the universe of discourse of each attribute into “n” intervals according to the number of fuzzy sets, also the algorithm calculates automatically the width (δi) according to the width (Δ). In phase two, we use the rough set techniques to reduce the number of attributes that comes from phase one and produce fuzzy-rough rules. In phase three, our algorithm automatically calculates the confidence (weight or fitness value) and support of each fuzzy rough rule then it calculates the total weight or fitness value of all linguistic rules. In phase four, we use the genetic algorithm on the fuzzy-rough rules from phase three then the algorithm automatically calculates the confidence and support of each fuzzy rough rule again then automatically calculates the total weight or fitness value of all rules. The result of fitness value of our algorithm that applied on Iris plants dataset before using our genetic algorithm is 0.28 but after using our algorithm the fitness value will be 0.97.