As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input sample to belong to m... more As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input sample to belong to more than one cluster, providing more flexibility than non-fuzzy clustering methods.
With the technology development, the need of analyze and extraction of useful information is incr... more With the technology development, the need of analyze and extraction of useful information is increasing. Bayesian networks contain knowledge from data and experts that could be used for decision making processes But they are not easily understandable thus the rule extraction methods have been used but they have high computation costs. To overcome this problem we extract rules from Bayesian network using genetic algorithm. Then we generate the graphical chain by mutually matching the extracted rules and Bayesian network. This graphical chain could shows the sequence of events that lead to the target which could help the decision making process. The experimental results on small networks show that the proposed method has comparable results with brute force method which has a significantly higher computation cost.
2014 Iranian Conference on Intelligent Systems (ICIS), 2014
Difficulties of tackling real-world problems with their growing complexities motivated computer s... more Difficulties of tackling real-world problems with their growing complexities motivated computer scientists to search for more efficient problem solving approaches. Metaheuristic algorithms are outstanding examples of these approaches. Bat Algorithm (BA) is a new meta-heuristic optimization algorithm, which has been developed rapidly and has been applied in different optimization tasks in recent years. In this paper an improved version of Bat algorithm with chaos is represented. The approach is based on the substitution of the random number generator (RNG) with chaotic sequences for parameter initialization. Simulation results on some mathematical benchmark functions demonstrate the validity of proposed algorithm, in which the Chaotic Bat Algorithm (CBA) outperforms the classical BA.
As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input sample to belong to m... more As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input sample to belong to more than one cluster, providing more flexibility than non-fuzzy clustering methods.
With the technology development, the need of analyze and extraction of useful information is incr... more With the technology development, the need of analyze and extraction of useful information is increasing. Bayesian networks contain knowledge from data and experts that could be used for decision making processes But they are not easily understandable thus the rule extraction methods have been used but they have high computation costs. To overcome this problem we extract rules from Bayesian network using genetic algorithm. Then we generate the graphical chain by mutually matching the extracted rules and Bayesian network. This graphical chain could shows the sequence of events that lead to the target which could help the decision making process. The experimental results on small networks show that the proposed method has comparable results with brute force method which has a significantly higher computation cost.
2014 Iranian Conference on Intelligent Systems (ICIS), 2014
Difficulties of tackling real-world problems with their growing complexities motivated computer s... more Difficulties of tackling real-world problems with their growing complexities motivated computer scientists to search for more efficient problem solving approaches. Metaheuristic algorithms are outstanding examples of these approaches. Bat Algorithm (BA) is a new meta-heuristic optimization algorithm, which has been developed rapidly and has been applied in different optimization tasks in recent years. In this paper an improved version of Bat algorithm with chaos is represented. The approach is based on the substitution of the random number generator (RNG) with chaotic sequences for parameter initialization. Simulation results on some mathematical benchmark functions demonstrate the validity of proposed algorithm, in which the Chaotic Bat Algorithm (CBA) outperforms the classical BA.
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Papers by Meysam Ghafari