Research Article
Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification
Table 8
Classification accuracies obtained by our method and other classifiers for the hepatitis dataset.
| Author (year) | Method | Classification accuracy (%) |
| Polat and Güneş (2006) [1] | FS-AIRS with fuzzy res. (10-fold CV) | 92.59 | Polat and Güneş (2007) [2] | FS-Fuzzy-AIRS (10-fold CV) | 94.12 |
Polat and Güneş (2007) [3] | AIRS (10-fold CV) | 76.00 | PCA-AIRS (10-fold CV) | 94.12 | Kahramanli and Allahverdi (2009) [4] | Hybrid system (ANN and AIS) (without k-fold CV) | 96.8 | Dogantekin et al. (2009) [5] | LDA-ANFIS | 94.16 | Bascil and Temurtas (2011) [6] | MLNN (MLP) + LM (10-fold CV) | 91.87 | Our study | ABCFS + SVM (10-fold CV) | 94.92 |
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