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The minimum redundancy-maximum relevance selection algorithm [42] was used to ensure good classification accuracy by selecting 40 better features from all features generated by SSGLDM.
This scheme, termed as minimum-redundancy-maximum-relevance selection [42], has been found to be more powerful than the maximum relevance selection.
Despite its relevance, selection for feed efficiency in beef cattle is a challenge in breeding programs, as it is necessary to measure daily feed intake and body weight gain individually for at least 70 days [ 7].
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This has been called maximum-relevance selection.
This is mainly due to the use of minimum redundancy-maximum-relevance selection [42] that reduces the number of features and ensure a good classification accuracy.
Then the features are carefully selected by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) methods.
The relevance of selection analyses with codon models for downstream applications can be demonstrated on a selection of case studies.
GO terms that may have functional relevance for selection in Thoroughbred are listed in Table 2.
mRMR conducts minimum redundancy maximum relevance feature selection.
This may be of relevance for selection of patients for adjuvant treatment.
Although a number of these tags probably have biological relevance, their selection from the SAGE data sets remains a challenge.
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