Feature Selection: Multi-source and Multi-view Data Limitations, Capabilities and Potentials

Marianne Cherrington; Joan Lu; David Airehrour; Fadi Thabtah; Qiang Xu; Samaneh Madanian

doi:10.1109/ITNAC46935.2019.9077992

Feature Selection: Multi-source and Multi-view Data Limitations, Capabilities and Potentials

Marianne Cherrington, Joan Lu, David Airehrour, Fadi Thabtah, Qiang Xu, Samaneh Madanian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

Abstract

Feature Selection (FS) is a crucial step in high-dimensional and big data analytics. It mitigates the 'curse of dimensionality' by removing redundant and irrelevant features. Most FS algorithms use a single source of data and struggle with heterogeneous data, yet multi-source (MS) and multi-view (MV) data are rich and valuable knowledge sources. This paper reviews numerous, emerging FS techniques for both these data types. The major contribution of this paper is to underscore uses and limitations of these heterogeneous methods concurrently, by summarising their capabilities and potentials to inform key areas of future research, especially in numerous applications.

Original language	English
Title of host publication	29th International Telecommunication Networks and Applications Conference
Subtitle of host publication	(ITNAC 2019)
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9781728136738, 9781728136721
ISBN (Print)	9781728136745
DOIs	https://doi.org/10.1109/ITNAC46935.2019.9077992
Publication status	Published - 1 Nov 2019
Event	29th International Telecommunication Networks and Applications Conference - Auckland, New Zealand Duration: 27 Nov 2019 → 29 Nov 2019 Conference number: 29

Publication series

Name	2019 29th International Telecommunication Networks and Applications Conference, ITNAC 2019
Publisher	IEEE
ISSN (Print)	2474-1531
ISSN (Electronic)	2474-154X

Conference

Conference	29th International Telecommunication Networks and Applications Conference
Abbreviated title	ITNAC 2019
Country/Territory	New Zealand
City	Auckland
Period	27/11/19 → 29/11/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ITNAC46935.2019.9077992Licence: Unspecified

Cite this

Cherrington, M., Lu, J., Airehrour, D., Thabtah, F., Xu, Q., & Madanian, S. (2019). Feature Selection: Multi-source and Multi-view Data Limitations, Capabilities and Potentials. In 29th International Telecommunication Networks and Applications Conference : (ITNAC 2019) [9077992] (2019 29th International Telecommunication Networks and Applications Conference, ITNAC 2019). IEEE. https://doi.org/10.1109/ITNAC46935.2019.9077992

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keywords = "feature selection (FS), heterogeneous data, machine learning, multi-source data (MSD), multi-view data (MVD)",

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Cherrington, M, Lu, J, Airehrour, D, Thabtah, F, Xu, Q & Madanian, S 2019, Feature Selection: Multi-source and Multi-view Data Limitations, Capabilities and Potentials. in 29th International Telecommunication Networks and Applications Conference : (ITNAC 2019)., 9077992, 2019 29th International Telecommunication Networks and Applications Conference, ITNAC 2019, IEEE, 29th International Telecommunication Networks and Applications Conference, Auckland, New Zealand, 27/11/19. https://doi.org/10.1109/ITNAC46935.2019.9077992

Feature Selection : Multi-source and Multi-view Data Limitations, Capabilities and Potentials. / Cherrington, Marianne; Lu, Joan; Airehrour, David et al.

29th International Telecommunication Networks and Applications Conference : (ITNAC 2019). IEEE, 2019. 9077992 (2019 29th International Telecommunication Networks and Applications Conference, ITNAC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Feature Selection (FS) is a crucial step in high-dimensional and big data analytics. It mitigates the 'curse of dimensionality' by removing redundant and irrelevant features. Most FS algorithms use a single source of data and struggle with heterogeneous data, yet multi-source (MS) and multi-view (MV) data are rich and valuable knowledge sources. This paper reviews numerous, emerging FS techniques for both these data types. The major contribution of this paper is to underscore uses and limitations of these heterogeneous methods concurrently, by summarising their capabilities and potentials to inform key areas of future research, especially in numerous applications.

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KW - multi-source data (MSD)

KW - multi-view data (MVD)

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Cherrington M, Lu J, Airehrour D, Thabtah F, Xu Q, Madanian S. Feature Selection: Multi-source and Multi-view Data Limitations, Capabilities and Potentials. In 29th International Telecommunication Networks and Applications Conference : (ITNAC 2019). IEEE. 2019. 9077992. (2019 29th International Telecommunication Networks and Applications Conference, ITNAC 2019). doi: 10.1109/ITNAC46935.2019.9077992

Feature Selection: Multi-source and Multi-view Data Limitations, Capabilities and Potentials

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