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Rule Induction of Automotive Historic Styles Using Decision Tree Classifier Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Small Samples of Multidimensional Feature Vectors

Author : Leon Bobrowski

Published in: Advances in Computational Collective Intelligence

Publisher: Springer International Publishing

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Abstract

A small sample of multidimensional feature vectors appears when the number of features is much greater than the number of objects (feature vectors).
For example, such circumstances appear typically in genetic data sets. In such cases, feature clustering can become a useful tool in classification or prognosis tasks. Feature clustering can be performed through the minimization of the convex and piecewise linear (CPL) criterion functions.

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previous chapter K-Means Clustering for Features Arrangement in Metagenomic Data Visualization
next chapter Using Fourier Series to Improve the Discrete Grey Model (1, 1)
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Bobrowski L.: Data Exploration and Linear Separability, pp. 1–172. Lambert Academic Publishing (2019)
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Bobrowski, L.: Data mining based on convex and piecewise linear (CPL) criterion functions (in Polish). Bialystok University of Technology Press (2005)
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Bobrowski, L., Łukaszuk, T.: Relaxed linear separability (RLS) approach to feature (Gene) subset selection. In: Xia, X. (ed.) Selected Works in Bioinformatics, pp. 103–118. INTECH (2011)
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Bobrowski, L.: Design of piecewise linear classifiers from formal neurons by some basis exchange technique. Pattern Recognit. 24(9), 863–870 (1991)CrossRef
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Metadata
Title
Small Samples of Multidimensional Feature Vectors
Author
Leon Bobrowski
Copyright Year
2020
Book
Advances in Computational Collective Intelligence

Print ISBN: 978-3-030-63118-5

Electronic ISBN: 978-3-030-63119-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63119-2_8

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