A GA-based feature selection and parameters optimizationfor support vector machines
By Cheng-Lung Huang, Chieh-Jen Wang. Expert systems with applications, 2006
1,462 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/f5736b335a8b0ae3bdf8523f37e8fef5051256ad
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Saoussen Krichen
- Chaouki Khammassi
- Bo Yang
- Chih-Fong Tsai
- Shih-Lung Shaw
- Long Chen
- Wenchang Kang
- Da-you Liu
In Computer Vision and Pattern Recognition
- Bo Yang
- Chih-Fong Tsai
- Shih-Lung Shaw
- Saoussen Krichen
- Long Chen
- Wenchang Kang
- Da-you Liu
- Huaizhong Li
Citations in the corpus
- A GA-LR wrapper approach for feature selection in network intrusion detection (2017, background): “The fitness function of a given chromosome determines its chance of being chosen to create the next generation (Huang and Wang, 2006).”
- Evolutionary algorithms for hyperparameter tuning on neural networks models (2014, methodology): “In hyperparameter tuning problems, EA have been proved to perform better than grid search techniques based on accuracy-speed ratio (Martino et al. 2011; Friedrichs and Igel 2005; Huang and Wang 2006).”
- Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches (2010, background): “Associated with the characteristics of exploitation and exploration search, GA can deal with large search spaces efficiently, and hence has less chance to get a local optimal solution than other algorithms [16].”
- Evolving support vector machines using fruit fly optimization for medical data classification (2016, background): “Recently, biologically-inspired metaheuristics (such as he genetic algorithm [17], particle swarm optimization (PSO) [18], nd bacterial foraging optimization (BFO) [19]) have been shown o be more likely to determine the global optimum solution than he traditional aforementioned methods.”
- A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios (2014, methodology): “The accuracy of the GA-based SVM method reached 88%, which is a reasonable accuracy value for complicated road conditions because some false points are discarded in the next step (see [59] for more details on GA-based parameter optimization).”