Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Combining the mutual information criterion with a forward feature selection strategy offers a good trade-off beten optimality of the selected feature subset and computation time. Hover, it requires to set the parameter(s) of the mutual information estimator and to determine when to halt the forward procedure. These two choices are difficult to make because, as the dimensionality of the subset increases, the estimation of the mutual information becomes less and less reliable.  proposes to use resampling methods, a K-fold cross-validation and the permutation test, to address both issues. The resampling methods bring information about the variance of the estimator, information which can then be used to automatically set the parameter and to calculate a threshold to stop the forward procedure. The procedure is illustrated on a synthetic data set as ll as on the real-world examples.
['Damien François', 'Fabrice Rossi', 'Vincent Wertz', 'Michel Verleysen']
I am writing a research paper on a idea Resampling methods for parameter-free and robust feature selection with mutual information
[{"from": "human", "value": "I am writing a research paper on a idea Resampling methods for parameter-free and robust feature selection with mutual information"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Combining the mutual information criterion with a forward feature selection strategy offers a good trade-off beten optimality of the selected feature subset and computation time. Hover, it requires to set the parameter(s) of the mutual information estimator and to determine when to halt the forward procedure. These two choices are difficult to make because, as the dimensionality of the subset increases, the estimation of the mutual information becomes less and less reliable.  proposes to use resampling methods, a K-fold cross-validation and the permutation test, to address both issues. The resampling methods bring information about the variance of the estimator, information which can then be used to automatically set the parameter and to calculate a threshold to stop the forward procedure. The procedure is illustrated on a synthetic data set as ll as on the real-world examples."}]