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2023 | OriginalPaper | Buchkapitel

Detection of Sparsity in Multidimensional Data Using Network Degree Distribution and Improved Supervised Learning with Correction of Data Weighting

verfasst von : Shinya Ueno, Osamu Sakai

Erschienen in: Complex Networks and Their Applications XI

Verlag: Springer International Publishing

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Abstract

Multidimensional data are representatives in a wide range of applications, from those in the latest state-of-the-art science and technology to specific social issues. And they have been subject to analysis using methods such as regression analysis and machine learning. However, they are rarely obtained as complete data and contain more or less biases and deficiencies. In this study, we form a network from a multidimensional dataset and use its degree distribution to detect data sparsity. Although model analysis based on the degree distribution has been conducted for many years, sparsity detection has not been a target of the degree distribution analysis. Furthermore, we attempt to increase the accuracy and precision of supervised learning by applying regressive weighting according to node grouping in the degree distribution spectrum. By making use of this algorithm, we can expand the range of utilization of incomplete data together with other promising progresses in complex networks.

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Titel: Detection of Sparsity in Multidimensional Data Using Network Degree Distribution and Improved Supervised Learning with Correction of Data Weighting
verfasst von: Shinya Ueno
Osamu Sakai
Verlag: Springer International Publishing
Buch: Complex Networks and Their Applications XI
Print ISBN: 978-3-031-21126-3

Electronic ISBN: 978-3-031-21127-0

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-21127-0_32

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