S
Shih-Wei Lin
Researcher at Chang Gung University
Publications - 140
Citations - 6110
Shih-Wei Lin is an academic researcher from Chang Gung University. The author has contributed to research in topics: Simulated annealing & Job shop scheduling. The author has an hindex of 41, co-authored 140 publications receiving 5278 citations. Previous affiliations of Shih-Wei Lin include Ming Chi University of Technology & Memorial Hospital of South Bend.
Papers
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Particle swarm optimization for parameter determination and feature selection of support vector machines
TL;DR: Experimental results demonstrate that the classification accuracy rates of the developed approach surpass those of grid search and many other approaches, and that the developed PSO+SVM approach has a similar result to GA+S VM, Therefore, the PSO + SVM approach is valuable for parameter determination and feature selection in an SVM.
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Parameter determination of support vector machine and feature selection using simulated annealing approach
TL;DR: Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches, and the SA-SVM is thus useful for parameter determination and feature selection in the SVM.
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A simulated annealing heuristic for the capacitated location routing problem
TL;DR: A simulated annealing (SA) based heuristic for solving the location routing problem is proposed and it is indicated that the proposed SALRP heuristic is competitive with other well-known algorithms.
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An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection
TL;DR: In the proposed algorithm, SVM and SA can find the best selected features to elevate the accuracy of anomaly intrusion detection and the best parameter settings for the DT and SVM are automatically adjusted by SA.
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Solving the truck and trailer routing problem based on a simulated annealing heuristic
TL;DR: This study applies a simulated annealing (SA) heuristic to the truck and trailer routing problem (TTRP) and obtained 17 best solutions to the 21 benchmark TTRP benchmark problems, including 11 new best solutions.