Fuzzy rule has been used extensively in data mining.
This paper presents a fast and flexible method based on genetic
algorithm to construct fuzzy decision rule with considering
criteria of accuracy. First, the algorithm determines the width
that divides each attribute into “n” intervals according to the
number of fuzzy sets, after that calculates the parameters width
according to that width. Rough Sets Model Based on Database
Systems technique used to reduce the number of attributes if
there exists then we use the algorithm for extracting initial fuzzy
rules from fuzzy table using SQL statements with a smaller
number of rules than the other models without needing to use a
genetic algorithm – Based Rule Selection approach to select a
small number of significant rules, then it calculates their
accuracy and the confidence.. Multiobjective evolutionary
algorithms (EAs) that use nondominated sorting and sharing
have been criticized mainly for computational complexity and
needing for specifying a sharing parameter but in our genetic
model each fuzzy set represented by “Real number” from 0 to 9
forming a gene on chromosome (individual). Our genetic model
is used to improve the accuracy of the initial rules and calculates
the accuracy of the new rules again which be higher than the old
rules The proposed approach is applied on the Iris dataset and
the results compared with other models: Preselection with
niches, ENORA and NSGA to show its validity.
Parallel Wavegan: A Fast Waveform Generation Model Based on Generative Adversarial Networks with Multi-Resolution Spectrogram
