|
| Author | Feature selection/extraction method | Classifier used | Accuracy (%) |
|
| Cho et al. [8] (2003) | | Kernel fisher feature discriminant analysis (KFDA) | 73.53 |
|
|
Deb and Raji Reddy [9] (2003) | | NSGA-II | 100 |
|
| Lee et al. [10] (2003) | Bayesian model | Artificial neural network (ANN), KNN, and SVM | 97.05 |
|
| Ye et al. [11] (2004) | Uncorrelated linear discriminant analysis (ULDA) | KNN () | 97.5 |
|
| Cho et al. [12] (2004) | SVM-RFE | Kernel KFDA | 94.12 |
|
|
Paul and Iba [13] (2004) | Probabilistic model building genetic algorithm (PMBGA) | Naive-Bayes (NB), weighted voting classifier | 90 |
|
|
Daz and De andres [14] (2006) | | Random forest | 95 |
|
| Peng et al. [15] (2007) | Fisher ratio | NB, decision tree J4.8, and SVM | 100, 95.83, and 98.6 |
|
| Pang et al. [16] (2007) | Bootstrapping consistency gene selection | KNN | 94.1 |
|
| Hernandez et al. [17] (2007) | Genetic algorithm (GA) | SVM | 91.5 |
|
| Zhang and Deng [18] (2007) | Based Bayes error filter (BBF) | Support vector machine (SVM), -nearest neighbor (KNN) | 100, 98.61 |
|
| Bharathi and Natarajan [19] (2010) | ANOVA | SVM | 97.91 |
|
|
Tang et al. [20] (2010) | ANOVA | Discriminant Kernel partial least square (Kernel-PLS) | 100 |
|
|
Mundra and Rajapakse [7] (2010) | -test, SVM based -statistics, SVM with recursive feature elimination (RFE), and SVM based -statistic with RFE | SVM | 96.88, 98.12, 97.88, and 98.41 |
|
|
Lee and Leu [21] (2011) | -test | Hybrid with GA + KNN and SVM | 100 |
|
|
Salem et al. [22] (2011) | Multiple scoring gene selection technique (MGS-CM) | SVM, KNN, and linear discriminant analysis (LDA) | 90.97 |
|
