Table 4 ML methods applied in the reviewed papers
From: Survey of feature selection and extraction techniques for stock market prediction
ML methods | Number of articles | Research articles |
|---|---|---|
1. Linear regression | 1 | Rana et al. (2019) |
2. Naive bayes | 3 | Alsubaie et al. (2019); Nabi et al. (2019; Singh and Khushi (2021) |
3. Gaussian Naive bayes | 1 | Ampomah et al. (2021) |
(GNB) | ||
4. K-nearest neighbors | 2 | |
5. Lasso estimate | 1 | Aloraini (2015) |
6. Broad learning system | 1 | Li et al. (2022) |
(BLS) | ||
7. SVM | 11 | Cai et al. (2012; Alsubaie et al. (2019); Kumar et al. (2016); Nabi et al. (2019); Yuan et al. (2020); Labiad et al. (2016), |
Rana et al. (2019); Chen and Hao (2017); Siddique and Panda (2019); Singh and Khushi (2021); Iacomin (2015) | ||
Tree-based ML methods | - | - |
8. Decision tree | 3 | Nabi et al. (2019); Singh and Khushi (2021); Qolipour et al. (2021) |
9. RF | 6 | Nabi et al. (2019); Yuan et al. (2020); Labiad et al. (2016); Singh and Khushi (2021); Ampomah et al. (2020); Qolipour et al. (2021) |
10. Gradient boosted tree | 2 | |
Neural network methods | – | – |
11. ELM | 1 | Das et al. (2019) |
12. OSELM | 1 | Das et al. (2019) |
13. RBPNN | 1 | Das et al. (2019) |
14. Deep generative model | 1 | Haq et al. (2021) |
15. ANN | 2 | |
16. LSTM | 3 | Botunac et al. (2020); Shen and Shafiq (2020); Rana et al. (2019) |