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.
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Acknowledgments
The presented study was supported by the grant S/WI/2/2018 from Bialystok University of Technology and funded from the resources for research by Polish Ministry of Science and Higher Education.
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Bobrowski, L., Łukaszuk, T. (2019). Feature (Gene) Selection in Linear Homogeneous Cuts. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_24
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DOI: https://doi.org/10.1007/978-3-030-17935-9_24
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