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The aims of proposed feature selection algorithm is to maximize the relevancy and minimize the redundancy criterion based on Pearson's correlation (MRMRPC) coefficient.
To minimize the redundancy while meeting the E2E availability requirement, partial restoration is defined and embedded in the developed model, by which a novel parameter, called a protection level, is manipulated.
It involves a calculation of a covariance matrix of the dataset to minimize the redundancy and maximize the variance.
At each step, a new feature is created that attempts to maximize the MI between the new feature and the target class and to minimize the redundancy.
Mutual information based feature selection algorithm was initially proposed by Battiti [73] to maximize the relevance between the input features and the output and to minimize the redundancy of the selected features.
Based on supervised learning and similarity measurements, we propose a Recursive Feature Addition (RFA), recursively employ supervised learning to obtain the highest training accuracy and add a subsequent gene based on the similarity between the chosen features and the candidates to minimize the redundancy within the feature set.
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As a result, this approach selects the most relevant genes to the target classes and minimizes the redundancy among the selected genes to define an optimal gene set which provides the highest classification accuracy.
With this standardized genotype panel, cotton SSR markers derived from different sources or groups can be evaluated in a systematic way to minimize the potential redundancy and to determine the markers' Polymorphic Information Content (PIC) values for ready applications.
This problem can be addressed by modifying either the statistical test or the gene-sets to minimize the effect of redundancy and produce more concise enrichment results.
In order to minimize the gap and redundancy in item contents, the item difficulties should be evenly distributed to cover the entire test [ 32].
To ensure that the ortholog groups selected for this study contain sufficiently diverse organisms (to minimize the impact of redundancy during entropy calculations), we considered only those COGs containing organisms listed in Table 4.
More suggestions(15)
reducing the redundancy
limit the redundancy
minimize the probability
minimize the conversation
minimize the weeping
minimize the fact
minimize the risk
minimize the pain
minimize the gluttony
minimize the damage
minimize the trauma
minimize the difficulty
minimize the impact
minimize the surprise
minimize the number
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