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Prior to classification, detailed statistical analysis viz., test of significance, density estimation have been performed for identifying discriminating power of the features in between malignant and benign classes.
The relative predictive power of the features is measured through a feature selection algorithm.
The classification accuracy, and the discriminatory power of the features extracted determine the success of such retrieval systems.
Furthermore, as explained in Section 3.1 that the co-occurrence of features not only increases the discriminative power of the features but also significantly reduces the training time by reducing the number of weak classifiers to half.
A classifier can provide a criterion to evaluate the discrimination power of the features for the feature subset selection.
Therefore, we evaluate the discriminative power of the features based on different λ varying from 1 to 5.
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Still, the data does provide a window into the power of the featured position.
One can clearly observe the significant separation between the group spaces explaining the high-discriminatory power of the feature set based on the octave distribution.
For two features that are highly dependent on each other, removing any one of them will not bring about much change in the class discriminative power of the feature.
Anna Lowman of TV Scoop noted that some of the power of the featured Doctor Who music came from the audience associating the themes with powerful scenes from the television series.
By doing this, MRMR expands the representative power of the feature set and improves their generalization properties.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com