SFLA IWSSr

The SFLA IWSSr software is a hybrid feature selection method for high-dimensional datasets, particularly gene expression data. It combines the Shuffled Frog Leaping Algorithm (SFLA) and the IWSSr method to select the most effective features while maintaining high classification accuracy.

The tool operates in two phases:

1. Filtering phase: The Relief method is employed to assign weights to the features based on their relevance to the classification task.

2. Wrapping phase: SFLA and IWSSr algorithms are used to search for the most effective features within the feature-rich space. SFLA is a meta-heuristic optimization algorithm inspired by the natural memetic evolution of frogs, while IWSSr is a feature selection method that considers both feature relevance and redundancy.

The combination of these techniques allows SFLA IWSSr to extract a compact set of features that are most informative for the classification task, thereby reducing the dimensionality of the dataset and improving the classification performance. The tool has been evaluated using standard gene expression datasets, and the experimental results demonstrate that it outperforms similar methods in terms of feature set compactness and classification accuracy.