Multi-Label classifier based on Kernel Random Vector Functional Link Network

Vikas Chauhan; Aruna Tiwari; Shivvrat Arya

doi:10.1109/IJCNN48605.2020.9207436

Multi-Label classifier based on Kernel Random Vector Functional Link Network

Vikas Chauhan
, Aruna Tiwari
, Shivvrat Arya

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

9 Scopus citations

Abstract

In this paper, a kernelized version of the random vector functional link network is proposed for multi-label classification. This classifier uses pseudoinverse to find output weights of the network. As pseudoinverse is non-iterative in nature, it requires less fine-tuning to train the network. Kernelization of RVFL makes it robust and stable as no need to tune the number of neuron in the enhancement layer. A threshold function is used with a kernelized random vector functional link network to make it suitable for multi-label learning problems. Experiments performed on three benchmark multi-label datasets bibtex, emotions, and scene shows that proposed classifier outperforms various the existing multi-label classifiers.

Original language	English (US)
Title of host publication	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728169262
DOIs	https://doi.org/10.1109/IJCNN48605.2020.9207436
State	Published - Jul 2020
Externally published	Yes
Event	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom Duration: Jul 19 2020 → Jul 24 2020

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/Territory	United Kingdom
City	Virtual, Glasgow
Period	7/19/20 → 7/24/20

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Keywords

kernel random vector functional link
multi-label classification
non-iterative neural networks
pseudoinverse
Random vector functional link

Access to Document

10.1109/IJCNN48605.2020.9207436

Cite this

Chauhan, V., Tiwari, A., & Arya, S. (2020). Multi-Label classifier based on Kernel Random Vector Functional Link Network. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings Article 9207436 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN48605.2020.9207436

@inproceedings{17d4992c451949f9b9683357b2904963,

title = "Multi-Label classifier based on Kernel Random Vector Functional Link Network",

abstract = "In this paper, a kernelized version of the random vector functional link network is proposed for multi-label classification. This classifier uses pseudoinverse to find output weights of the network. As pseudoinverse is non-iterative in nature, it requires less fine-tuning to train the network. Kernelization of RVFL makes it robust and stable as no need to tune the number of neuron in the enhancement layer. A threshold function is used with a kernelized random vector functional link network to make it suitable for multi-label learning problems. Experiments performed on three benchmark multi-label datasets bibtex, emotions, and scene shows that proposed classifier outperforms various the existing multi-label classifiers.",

keywords = "kernel random vector functional link, multi-label classification, non-iterative neural networks, pseudoinverse, Random vector functional link",

author = "Vikas Chauhan and Aruna Tiwari and Shivvrat Arya",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

month = jul,

doi = "10.1109/IJCNN48605.2020.9207436",

language = "English (US)",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings",

address = "United States",

}

Chauhan, V, Tiwari, A & Arya, S 2020, Multi-Label classifier based on Kernel Random Vector Functional Link Network. in 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings., 9207436, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., 2020 International Joint Conference on Neural Networks, IJCNN 2020, Virtual, Glasgow, United Kingdom, 7/19/20. https://doi.org/10.1109/IJCNN48605.2020.9207436

Multi-Label classifier based on Kernel Random Vector Functional Link Network. / Chauhan, Vikas; Tiwari, Aruna; Arya, Shivvrat.
2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9207436 (Proceedings of the International Joint Conference on Neural Networks).

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

TY - GEN

T1 - Multi-Label classifier based on Kernel Random Vector Functional Link Network

AU - Chauhan, Vikas

AU - Tiwari, Aruna

AU - Arya, Shivvrat

PY - 2020/7

Y1 - 2020/7

N2 - In this paper, a kernelized version of the random vector functional link network is proposed for multi-label classification. This classifier uses pseudoinverse to find output weights of the network. As pseudoinverse is non-iterative in nature, it requires less fine-tuning to train the network. Kernelization of RVFL makes it robust and stable as no need to tune the number of neuron in the enhancement layer. A threshold function is used with a kernelized random vector functional link network to make it suitable for multi-label learning problems. Experiments performed on three benchmark multi-label datasets bibtex, emotions, and scene shows that proposed classifier outperforms various the existing multi-label classifiers.

AB - In this paper, a kernelized version of the random vector functional link network is proposed for multi-label classification. This classifier uses pseudoinverse to find output weights of the network. As pseudoinverse is non-iterative in nature, it requires less fine-tuning to train the network. Kernelization of RVFL makes it robust and stable as no need to tune the number of neuron in the enhancement layer. A threshold function is used with a kernelized random vector functional link network to make it suitable for multi-label learning problems. Experiments performed on three benchmark multi-label datasets bibtex, emotions, and scene shows that proposed classifier outperforms various the existing multi-label classifiers.

KW - kernel random vector functional link

KW - multi-label classification

KW - non-iterative neural networks

KW - pseudoinverse

KW - Random vector functional link

UR - https://www.scopus.com/pages/publications/85093845636

UR - https://www.scopus.com/pages/publications/85093845636#tab=citedBy

U2 - 10.1109/IJCNN48605.2020.9207436

DO - 10.1109/IJCNN48605.2020.9207436

M3 - Conference contribution

AN - SCOPUS:85093845636

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020

Y2 - 19 July 2020 through 24 July 2020

ER -

Chauhan V, Tiwari A, Arya S. Multi-Label classifier based on Kernel Random Vector Functional Link Network. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9207436. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN48605.2020.9207436

Multi-Label classifier based on Kernel Random Vector Functional Link Network

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All Science Journal Classification (ASJC) codes

Keywords

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