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Three issues exist in the mutual information based scheme proposed in "Mutual information based feature selection and sampling rate adjustment".
In "Mutual information based feature selection and sampling rate adjustment", a mutual information based feature selection and sampling rate adjustment scheme have been proposed.
Mutual information, based on information theory, can be a good candidate [19].
A total of 65 conditional mutual information based features are, therefore, considered.
Mutual information based technique for feature selection is proposed to enhance overall design of neural network.
Second, the mutual information based scheme involves a lot of parameter adjustment, which is hard to use in practice.
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Specifically, ScanBMA generally produced more favorable areas under the Receiver-Operating Characteristic and Precision-Recall curves than other regression-based methods and mutual-information based methods.
To select the features satisfying this purpose, we propose a Mutual-information based Feature Selection (MFS) algorithm.
Introducing some biological parameters, such as the physico-chemical properties of amino acid sites may dramatically increase the sensitivity of mutual-information based methods to detect coevolution.
The work left from translation estimation is then determined by mutual information-based [ 28], phase correlation [ 26, 27] based, or cross correlation [ 29] based template matching.
Accordingly, a mutual information-based method was developed for identifying coevolved protein residues and DNA bases.
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