Table 2.
Comparison of different FSA methods
Methods | Description | Classifier dependency | Computational cost | Applicable scenarios | Challenges |
---|---|---|---|---|---|
Filter | Complete featureselection based onevaluation functionbefore classification. | Independent | Low | High-dimensional datasets and datapreprocessing | No guarantee to find theoptimal subset. |
Wrapper | Select the optimalfeature subset basedon training resultsof classifier. | Dependent | High | Not applicable tohigh-dimensional datasets | Poor generalization ability and long computation time. |
Embedded | Integrate the featureselection and modeltraining. | Dependent | Low | High-dimensional datasets | Possibility of overfitting. |
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