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And then design a distance-based separability weighting strategy to conduct feature level fusion.
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Two novel incremental feature weighting strategies are proposed in this paper, one based on a leave-one-feature-out, the other based on a feature-wise separability criterion.
Since 3 pairs of genomes are not separated while the other 7 are separated, the separability rate is 70%.
The global recognition rate of the multibiometric system can be improved by incorporating these separability measures as integration weights in the fusion module.
The global recognition rate of the multibiometric system can be improved by incorporating the reliability and separability measures as integration weights in the fusion module.
In addition to demonstrating the linear separability, the phenotype separations provided by the three biomarkers give very meaningful biological information.
For improving the recognition performance of the multibiometric system, we have presented a multi-normalization-based integration weight estimation scheme using separability measures in [22, 37].
Separability implies that spacelike separated systems have associated with them independent real states of affairs.
Thus, in [15], a t f region is selected by a two-dimensional weighting function based on a mutual information criterion developed to obtain the maximum separability among classes, so the weighted space is mapped to a set of one-dimensional features, although the methodology is restricted to a specific class of t f representations.
In this paper, we propose an efficient integration weight optimization strategy incorporating both the reliability measures from the score space (dispersion measure) and the separability measures from the feature space (inter-/intra-class distance) and score space (d-prime statistic).
The molecules may have low or extremely high molecular weight, and they may be branched or linear in structure, but the essential feature is that of separability and consequent mobility.
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