Common feature selection algorithms implemented in Java, including
- Sequential Forward Selection (SFS)
- Sequential Backward Selection (SBS)
- Sequential Forward Floating Selection (SFFS)
- Sequential Backward Floating Selection (SFBS)
This uses a Wrapper approach, utilising the Weka library as a classifier.
Datasets used for testing:
Sample output from WINE dataset on random training:testing (70:30) split of the data
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Sequential backward floating selection for 10 features
Accuracy using all features: 47.059%
Accuracy using features ([2, 3, 6, 7, 8, 9, 10, 11]): 94.118%
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Sequential backward floating selection
Accuracy using all features: 41.176%
Accuracy using features ([0, 1, 2, 8, 9, 11]): 88.235%
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Sequential floating forward selection for 5 features
Accuracy using all features: 94.118%
Accuracy using features ([1, 2, 12]): 94.118%
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Sequential forward selection
Accuracy using all features: 82.353%
Accuracy using features ([0, 1, 6, 10]): 88.235%
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Sequential backward selection
Accuracy using all features: 64.706%
Accuracy using features ([3, 5, 9, 10, 11]): 82.353%
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Sequential forward selection for max 10 features
Accuracy using all features: 82.353%
Accuracy using features ([0, 6, 7, 9]): 100.000%
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Sequential backward selection for max 10 Features
Accuracy using all features: 47.059%
Accuracy using features ([6, 9, 11]): 88.235%
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Sequential floating forward selection
Accuracy using all features: 41.176%
Accuracy using features ([1, 5, 8, 9, 10]): 76.471%
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