## 3 Feature Selection Algorithms
### Empirical Mode Decomposition
#### 4.2.3 Support Vector Machines (SVMs)
Grid-search, PSO, and GA were used to optimize the SVM hyperparameters in structural damage identification, and the selected damage features included selected autoregressive and residual error features. The application of the above techniques showed improved SVM prediction performance, and the GA with AR features shows the best classification results. [PERSON] and [PERSON] [122] proposed a new method for structural damage identification using an SVM with a newly proposed kernel. A Littlewood-Paley wavelet kernel was presented to improve the accuracy of the SVM for the SHM damage classification task, and the social harmony search algorithm was used to optimize the hyperparameters of the SVM. The results improved damage identification accuracy with the proposed kernel compared to other kernels combined. An LS-SVM-based damage identification method was proposed by [PERSON] [123] using incomplete modal data and natural frequencies as training samples and an iterated improved reduction system method. Coupled simulated annealing (CSA) was used to determine the optimal LS-SVM fitting parameters combined with a 10-fold cross-validation method. The experimental validation of the method was performed by applying it to three different structures, and the results showed an acceptable performance of the LS-SVM in damage identification. [PERSON] et al. [27] conducted a study in which four kernel-based ML models for damage identification tasks under various noise conditions were compared to analyze their performance. Among the kernel-based ML models, one-class support vector machine, support vector data description, kernel principal component analysis and greedy kernel principal component analysis were selected and used for damage identification. Compared with the previous studies performed by other ML models, kernel-based damage identification showed better accuracy results and better generalization ability. [PERSON] et al. [42] presented an a posteriori probability support vector machine based on Dempster-Shafer evidence theory combined with a multi-sensor data fusion strategy. Signal energy features were extracted from vibration data using WPT and then processed by a posteriori probability support vector machine (PPSVM). The experimental results demonstrated that the proposed method was a robust structural damage identification method due to the fusion of information sensors and the application of the Dempster-Shafer evidence theory. [PERSON] et al. [124] proposed a beam structural damage identification method for crack detection using a BP-ANN model and an SVM. The training data were generated through a FE beam model, and the strain differences between the models were considered damage indicators. The two ML models were used and compared, and the results indicated that both ML models performed well in crack identification for single and two-crack damage cases. In the SHM damage identification approach presented by [PERSON] et al. [32], random projection (RP) was used to reduce the size of the vibration data, thereby reducing the computational processing time in structural monitoring. Data anomalies or structural damage were detected through one-class SVM and SVM models using the reduced bridge data. The results showed that the combination of SVM and RP improves the computational speed without affecting the detection accuracy. [PERSON] et al. [43] presented a comparative study between ANN and LS-SVM for structural damage identification. The wavelet power spectrum was used for feature extraction, and the particle swarm harmony search (PSHS) algorithm was used for the hyperparameter selection of LS-SVM and ANN. The increased model performance of both ML techniques was achieved with the PSHS optimization method in terms of accuracy, and the combination of PSHS and LS-SVM was better than ANN with PSHS. [PERSON] and [PERSON] [125] designed an SVM-based method using only the first natural frequencies for damage identification and localization in shear structures as input data.