Sentence examples for mutual information ranking from inspiring English sources

The major task of the mutual information ranking chart is to enumerate the mutual information between features and activity/identity types at a specific sampling rate, and provide interactive ranking functionality.

Filter methods based on correlation or mutual information ranking [21] are easy to implement; however, selecting the most relevant variables is usually suboptimal for building a predictor, particularly if the variables are redundant.

The interface of the visualization tool, called VISEE, proposed in this work, is shown in Fig. 10, which includes five modules: (a) mutual information distribution diagram, (b) parallel coordinate map, (c) feature grid diagram, (d) mutual information ranking chart, (f) recommended solutions (modules (d) and (f) within the same switchable optional page).

The mutual information rank of the disease term was substantially higher in the AML patient group than in the healthy control group, which demonstrates that the proposed methodology can be successfully applied to infer associations between the personal genome and diseases.

Our algorithm generates the projections of the objects which are used to split the data by improved splitting criteria with rank mutual information (RMI) or rank Gini impurity (RGI).

We used normalized pointwise mutual information (NPMI) to rank genes that frequently co-occurred with key characteristics in biomedical literature.

The proposed integrated feature ranking and selection framework is performed in two stages: mutual information-based feature ranking and Lasso-based feature selection.

The first step in our framework is to perform feature ranking using mutual information.

Figure7 shows the mutual information as a function of rank.

Each method uses a different detection principle: SH applies χ2 or B statistics [ 32, 39]; BEAM uses Bayesian inference or B statistics; FIM, LRIT and LR are based on the logistic regression model; IG ranks SNPs by mutual information; MDR selects SNPs via prediction error; MECPM uses BIC to rank interactions and to assess statistical significance.

To mitigate the inconsistent feature selection issue of regularization, especially the lasso method [9], the framework preanalyzes all features to rank informative features based on mutual information scores [10].