Using Feature selection methods in high dimensionality of feature space are vital. In machine learning, we can use feature selection techniques in order to select proper subset of feature to increase machine learning accuracy. In this Repository we implemented various feature methods.
- Filter
- Wrapper
- Embeded
- Hybrid#
- Forward selection
- Backward elimination
- Forward stepwise selection
- Backward stepwise elimination
- Random mutation
- Chi-square
- Cross Entropy
- Fuzzy Entropy Measure
- Gini index
- Information Gain
- Mutual Information
- Relative Discrimination Criteria
- Term Strength
In order to use these code you need to send your data and classes in 2d dimention. also you need to pass your dataset type we provide varoius dataset in our github.