Table 1.
Classification algorithms employed for preferences detection in neuromarketing.
| References | Classification Algorithm | Class | Best accuracy (%) |
|---|---|---|---|
| Chew et al., 2016 | SVM | 1. Liked 2. Disliked |
75 |
| KNN | 80 | ||
| Kim et al., 2015 | SVM | 1. Preferred image 2. Unnoticed image |
83.64 |
| Hadjidimitriou and Hadjileontiadis, 2012, 2013 | KNN | 1. Liked 2. Disliked |
91.02 |
| Pan et al., 2013 | SVM | 1. Liked 2. Disliked |
74.77 |
| Moon et al., 2013 | Quadratic discriminant analysis | 1. Most preferred 2. Preferred 3. Less preferred 4. Least preferred |
97.39 |
| KNN | 97.99 | ||
| Teo et al., 2017, 2018a,b | DNN | 1. Liked 2. Disliked |
74.38 |
| SVM | 60.19 | ||
| Hakim et al., 2018 | Logistic Regression | 1. Most favored 2. Least favored |
67.32 |
| SVM | 68.50 | ||
| KNN | 59.98 | ||
| Decision trees | 63.34 | ||
| Yadava et al., 2017 | DNN | 1. Liked 2. Disliked | 60.10 |
| SVM | 62.85 | ||
| RF | 68.41 | ||
| HMM | 70.33 |