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This paper presents a classification technique for analyzing students' data based on genetic algorithm. The classification technique is modified using Fuzzy rules. The combination of genetic algorithm and fuzzy rules is employed to optimize the indices entropy and gini index. The solution obtained by this classification technique is an optimal way to classify the students' database. The results of...
In the present study, we introduce an experimental analysis conducted over two Multicriteria Decision Aid (MCDA) classification methods that have been successfully applied to real world problems. Different from other studies on MCDA classifiers, which put more emphasis on the development of new methods, this work compares the effectiveness of two MCDA nominal classifiers under the same conditions...
This paper proposes a novel idea based feature selection in the verification system of palmprint, which can realize the specific feature selection for different user using genetic algorithm (GA). In the stage of enrollment, discrete wavelet transforms (DWT) and statistical methods are first used for feature extraction. Then GA is employed for feature selection, which means that each user has a specific...
In this paper we propose a clustering ensemble algorithm based on genetic algorithm. The most important feature of our method is ability to extract the number of clusters. Genetic algorithms have been known as methods with high ability to find the solution of optimization problems. One of these problems is clustering, a process that receives a dataset as input and divides its members into several...
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