A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy
By Chih-Hung Wu, Gwo-Hshiung Tzeng, Yeong-Jia Goo, Wen-Chang Fang. Expert systems with applications, 2007
395 citations recorded by the source corpus.
Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/905b30392737de3dbc3ace17714937149b587472
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Accounting
- Antanas Verikas
- Zivile Kalsyte
- Marija Bacauskiene
- Qinghua Huang
- Adas Gelzinis
- Kai-Yu He
- Hui Li
- Jie Sun
In Artificial Intelligence
- Antanas Verikas
- Zivile Kalsyte
- Marija Bacauskiene
- Qinghua Huang
- Adas Gelzinis
- Kai-Yu He
- Hui Li
- Jie Sun
In Management Science and Operations Research
- Antanas Verikas
- Zivile Kalsyte
- Marija Bacauskiene
- Qinghua Huang
- Adas Gelzinis
- Kai-Yu He
- Hui Li
- Jie Sun
Citations in the corpus
- Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, methodology): “[128] integrated real-valued GA (RGA) with SVM, so that the optimal parameters of SVM can be automatically optimized by RGA considering the predictive accuracy and generalization ability simultaneously.”
- Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, methodology): “GA ? SVM GA used for: selecting SVM hyper-parameters (Chen and Hsiao 2008; Wu et al. 2007); selecting features and SVM hyper-parameters (Min et al.”