Table 4.
Comparative accuracy analysis for machine learning classifiers in Neuromarketing
| Classifiers | Neuromarketing studies | Average accuracy |
|---|---|---|
| Support Vector Machine (SVM) | Like/dislike classification for esthetic preference recognition among 3D objects (Chew et al.) [17] | 68% |
| Attention bias identification between targeted and non-targeted stimuli using NeoCube-based SNN architecture (Doborjeh et al.) [64] | 48.5% | |
| Like/dislike classification among e-commerce product (Yadava et al.) [18] | 62.85% | |
| Emotional valence recognition between excitement and boredom using EEG device and combining SVM, KNN, SVR, LR (Ogino and Mitsukura) [68] | 72.4% | |
| Purchase decision prediction from fMRI data using recursive cluster elimination-based support vector machine (RCE-SVM) (Wang et al.) [30] | 55.70% | |
| Facial emotion recognition using GSR sensor biometric data (Goyal and Singh) [54] | 81.65% | |
| Seven-emotion recognition using EEG signal (Bhardwaj et al.). Happiness and sadness classification accuracy reported here, respectively | 87.5%, 92.5% | |
| Color classification using EEG signal (Rakshit et al.) | 78.81% | |
| K-Nearest Neighbor (KNN) | Like/dislike classification for esthetic preference recognition among 3D objects (Chew et al.) [17] | 64% |
| Hidden Markov model (HMM) | Like/dislike classification among e-commerce product (Yadava et al.) [18]. Classification accuracy reported for male and female subject, respectively | 70.33%, 63.56% |
| Linear discriminant analysis (LDA) | Seven-emotion recognition using EEG signal (Bhardwaj et al.) [58]. Happiness and sadness classification accuracy reported here, respectively | 82.5, 87.5% |
| Like-/dislike classification using car stimuli and ERP signal (Wreissenger et al.) | 61% | |
| Naïve Bayes | Purchase decision prediction using Neural Impulse Actuator (NIA) device (Taqwa et al.) [73] | 48.5% |
| Artificial Neural Network | Consumer gender prediction using facial action coding (Gurbuj and Toga) [28] | 83.8% |
| TV advertisement liking recognition using EEG signal (Soria Morillo et al.) [43] | 80% | |
| TV advertisement liking recognition using EEG (Soria Morillo et al.) [40] | 80% | |
| Like/dislike classification among e-commerce products (Yadava et al.) [18] | 60% |