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 Genetic Algorithm and Ensemble Systems for Multi-biometric ...
3. Use ensemble systems as the fusion approach for the multi-biometric recognition; 4. Optimize the recognition process, using genetic algorithms in the preprocessing (adaptation of a cancellable transformation This paper is divided into nine sections and structured as follows. A review of related research is presented in Section 2. Multi-...
List of genetic algorithm applications - Wikipedia
Genetic Algorithm for Rule Set Production Scheduling applications , including job-shop scheduling and scheduling in printed circuit board assembly. [14] The objective being to schedule jobs in a sequence-dependent or non-sequence-dependent setup environment in order to maximize the volume of production while minimizing penalties such as tardiness.
Feature Selection Using Stochastic Search: An Application ...
The search is performed using stochastic sampling and the classification uses a support vector machine strategy. This approach is found to be better than genetic algorithm-based strategies for feature selection on several benchmark data sets. Applied to system identification, the algorithm supports subsequent decision making....
Optimizing Genetic Algorithm in Feature Selection for ...
a genetic algorithm for feature selection in a face recogni-tion system. K-Nearest Neighbor is used as the classifying algorithm in this approach. In each iteration of the genetic algorithm, the system switches the role of training and test data in order to prevent premature convergence to local min-imums. Obtained results from implementing the ......
1.13. Feature selection — scikit-learn 0.23.2 documentation
1.13.4. Feature selection using SelectFromModel¶. SelectFromModel is a meta-transformer that can be used along with any estimator that has a coef_ or feature_importances_ attribute after fitting. The features are considered unimportant and removed, if the corresponding coef_ or feature_importances_ values are below the provided threshold parameter. Apart from specifying the threshold ......
Evolutionary Algorithms for Feature Selection
Process 4: Evolutionary Feature Selection. Lastly, let’s look at the evolutionary approach for feature selection. We use a population size of 20 and stop the optimization after a maximum of 30 generations. The optimization runs slightly longer than forward selection or backward elimination.
Multi-Stage Feature Selection by Using Genetic Algorithms ...
The results obtained with the GA-based multi-stage approach for feature selection was compared to the selection through the random forest (RF) algorithm. RF is an algorithm based on decision trees that uses a bagging strategy for improving the variance by decreasing the correlation between the trees.
Ant Colony Optimization Based Feature Selection Method for ...
In order to increase the prediction performance, various feature selection methods were proposed for multi-channel EEG data. 58, 59 Some other studies underlined the performance of ACO as feature selection method comparing to principal component analysis, genetic algorithm, random tree generation and differential evolution methods. 27, 28, 36 ......
Reducing the Complexity of Multi-Dimensional LBP Texture ...
Keywords: Texture, local binary patterns, LBP, MD-LBP, feature selection, genetic algorithm. 1. Introduction Texture analysis and classification form an important part of many computer vision tasks including content-based image retrieval, face analysis, medical image analysis, mul-timedia content classification and annotation. While variou s ......
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