|
| Methods | Advantage | Disadvantage |
|
| SVM [16–19, 25, 29, 31] | SVM avoids the complexity of high-dimensional space and directly uses the kernel function of this space | SVM is difficult to implement for large training samples and determine the kernel function |
| RF [16, 19, 25–27, 31] | RF can handle very high-dimensional data without feature selection | RF may overfit on some noisy classification or regression problems |
| KNN [31] | The training time complexity of KNN is lower than the support vector machine (SVM) | The amount of calculation is large |
| Compared with naive Bayes (NB), it has no assumptions about the data, has high accuracy, and is insensitive to outliers | When the sample is unbalanced, the prediction accuracy of rare categories is low |
| NB [16, 32] | NB performs well on small-scale data, and the algorithm is relatively simple | The posterior probability is determined by the prior and the data, and then to determine the classification, so there is a certain error rate in the classification decision |
| LDA [16, 26] | LDA works better when the sample classification information depends on the mean rather than the variance | LDA is not suitable for dimensionality reduction of samples from non-Gaussian distributions and may overfit |
| XGBoost [31] | Regularization is added to the loss function to prevent overfitting | The split gain of many leaf nodes at the same level is low, and it is unnecessary to perform further splits, which may bring unnecessary overhead |
| Parallel computing makes the algorithm more efficient |
| Memory optimization |
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