 | 2009 |
| 10 |  | Shih-Wei Lin,
Shih-Chieh Chen:
PSOLDA: A particle swarm optimization approach for enhancing classification accuracy rate of linear discriminant analysis.
Appl. Soft Comput. 9(3): 1008-1015 (2009) |
| 9 |  | Shih-Wei Lin,
Yeou-Ren Shiue,
Shih-Chieh Chen,
Hui-Miao Cheng:
Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks.
Expert Syst. Appl. 36(9): 11543-11551 (2009) |
| 8 |  | Ching-Chiuan Lin,
Shih-Chieh Chen,
Nien-Lin Hsueh:
Adaptive embedding techniques for VQ-compressed images.
Inf. Sci. 179(1-2): 140-149 (2009) |
| 7 |  | Shih-Wei Lin,
Shih-Chieh Chen,
Wen-Jie Wu,
Chih-Hsien Chen:
Parameter determination and feature selection for back-propagation network by particle swarm optimization.
Knowl. Inf. Syst. 21(2): 249-266 (2009) |
| 2008 |
| 6 |  | Shih-Wei Lin,
Zne-Jung Lee,
Shih-Chieh Chen,
Tsung-Yuan Tseng:
Parameter determination of support vector machine and feature selection using simulated annealing approach.
Appl. Soft Comput. 8(4): 1505-1512 (2008) |
| 5 |  | Shih-Wei Lin,
Tsung-Yuan Tseng,
Shuo-Yan Chou,
Shih-Chieh Chen:
A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks.
Expert Syst. Appl. 34(2): 1491-1499 (2008) |
| 4 |  | Shih-Wei Lin,
Kuo-Ching Ying,
Shih-Chieh Chen,
Zne-Jung Lee:
Particle swarm optimization for parameter determination and feature selection of support vector machines.
Expert Syst. Appl. 35(4): 1817-1824 (2008) |
| 2007 |
| 3 |  | Shih-Wei Lin,
Shuo-Yan Chou,
Shih-Chieh Chen:
Meta-heuristic approaches for minimizing total earliness and tardiness penalties of single-machine schedulingwith a common due date.
J. Heuristics 13(2): 151-165 (2007) |
| 2006 |
| 2 |  | Lin Yu Tseng,
Shih-Chieh Chen:
A hybrid metaheuristic for the resource-constrained project scheduling problem.
European Journal of Operational Research 175(2): 707-721 (2006) |
| 2004 |
| 1 |  | Chi-Hung Tsai,
Huai-Kuang Tsai,
Shih-Chieh Chen,
Cheng-Yan Kao:
Disulfide Connectivity Prediction Using Support Vector Machine and Novel Features.
METMBS 2004: 391-395 |