Table 1.
False discovery rate for the synthetic datasets
| Synthetic 1 [60 × 30, C2] | Synthetic 2 [1,000 × 100, C2] | Synthetic 3 [1,000 × 500, C10] | Synthetic 4 [100 × 500, C8] | |
|---|---|---|---|---|
| GSO | 0 | 0 | 0 | 0.10 |
| mRMR Peng | 0.67 | 0 | 0.30 | 0.90 |
| mRMR Spearman | 0.33 | 0 | 0.20 | 0.60 |
| Information gain | 0.67 | 0 | 0.50 | 1 |
| RELIEF | 0.33 | 0.38 | 0.30 | 0.70 |
| CFS | 0.67 | 0.13 | 0.50 | 1 |
| CBF | 0.67 | 0.13 | 0.50 | 1 |
| SIMBA | 0.33 | 0.50 | 1 | 0.80 |
| LOGO | 0.67 | 0.25 | 0.30 | 0.70 |
| L1-LSMI | 0.33 | 0.13 | 0.60 | 0.90 |
| IAMB | 0 | 0 | 0 | 0.10 |
| HITON | 0.67 | 0 | 0.90 | 1 |
| JMI | 0.33 | 0 | 0.30 | 0.90 |
| DISR | 0.33 | 0 | 0.30 | 0.90 |
| QPFS | 0.67 | 0 | 0.30 | 0.90 |
| CMIM | 0.66 | 1 | 0.40 | 0.90 |
| CIFE | 0.33 | 1 | 0.50 | 1 |
| MIQ | 0.67 | 0.13 | 0.40 | 0.80 |
| SPECCMI | 0.67 | 1 | 0.30 | 1 |
| RRCT | 0 | 0 | 0 | 0.10 |
The design matrices are summarized in the form N×M [number of samples × number of features], and the following term indicates the problem and number of classes (e.g., C2 indicates that this is a classification problem with two classes). The presented results are the FDR scores for the number of true features in each of the datasets (see Figure 1 and also the description of the synthetic datasets for details).