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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Feature (Gene) Selection in Linear Homogeneous Cuts
verfasst von : Leon Bobrowski, Tomasz Łukaszuk
Erschienen in: Bioinformatics and Biomedical Engineering
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Abstract
A layer of formal neurons ranked based on given learning data sets can linearize these sets. This means that such sets become linearly separable as a result of transforming feature vectors forming these sets through the ranked layer. After the transformation by the ranked layer, each learning set can be separated by a hyperplane from the sum of other learning sets.
A ranked layer can be designed from formal neurons as a result of multiple homogenous cuts of the learning sets by separating hyperplanes. Each separating hyperplane should cut off a large number of feature vectors from only one learning set. Successive separating hyperplanes can be found through the minimization of the convex and piecewise-linear (CPL) criterion functions. The regularized CPL criterion functions can be also involved in the feature selection tasks during successive cuts.