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Articles by H. Yedjour
Total Records ( 2 ) for H. Yedjour
  D. Yedjour , H. Yedjour and A. Benyettou
  Genetic algorithms are very efficient in the problems of exploration and seem to be able to find the single optimal solution in a huge space of possible solutions. However, they are ineffective when it comes to finding the exact value of the optimum in this space. This is precisely what the exact optimization algorithms perform best. It is therefore normal to think of combining exact and genetic algorithm to find the exact value of the optimum. Mc-RULEGEN is a new system to extract symbolic rules from a trained neural network, based on two approaches genetic and exact. MC-RULEGEN consists of three major components: A multi-layer perceptron neural component, a genetic component, a simplification rules component based on Quine McCluskey algorithm. Our method is tested on breast cancer and iris databases, the computational results have shown that the performances of the rules extracted by MC-RULEGEN are very high.
  D. Yedjour , H. Yedjour and A. Benyettou
  Neural networks are very efficient in solving various problems but they have no ability of explaining their answers and presenting gathered knowledge in a comprehensible way. Two main approaches are used, namely the pedagogical one that treats a network as a black box and the local one that examines its structure. Because searching rules is similar to NP-hard problem it justifies an application of evolutionary algorithm to the rule extraction. Pedagogical approaches such as GA are insensitive to the number of units of neural networks as they see them as "black boxes" interested only their inputs and their outputs. In the study we describe new rule extraction method based on evolutionary algorithm called GenRGA. It uses logical rules and is composed of three (03) main parts: genetic module, neural networks module and rules simplification module. GenRGA is tested in experimental studies using different benchmark data sets from UCI repository. Comparisons with other methods show that the extracted rules are accurate and highly comprehensible.
 
 
 
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