On Twin Bounded Support Vector Machine with Pinball Loss

P. Anagha; S. Balasundaram

doi:10.1007/978-981-19-0840-8_13

On Twin Bounded Support Vector Machine with Pinball Loss

P. Anagha^*
, S. Balasundaram

^*Corresponding author for this work

School of Computer Engineering

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

1 Citation (Scopus)

Abstract

With the introduction of pinball loss for misclassification, we present a novel pinball loss-based twin bounded support vector machine (Pin-TBSVM) having regularization term, scatter and misclassification loss term to enhance noise robustness. Pin-TBSVM is a non-parallel classifier where two kernel generated surfaces are constructed by solving quadratic programming problems. Experimental results on fourteen benchmark datasets show that the proposed method achieves improved accuracy performance than the popular traditional methods considered for comparison clearly indicating the advantage of the proposed method.

Original language	English
Title of host publication	Advanced Machine Intelligence and Signal Processing
Editors	Deepak Gupta, Koj Sambyo, Mukesh Prasad, Sonali Agarwal
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	177-190
Number of pages	14
ISBN (Print)	9789811908392
DOIs	https://doi.org/10.1007/978-981-19-0840-8_13
Publication status	Published - 2022
Event	3rd International Conference on Machine Intelligence and Signal Processing, MISP 2021 - Jote, India Duration: 23-09-2021 → 25-09-2021

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	858
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	3rd International Conference on Machine Intelligence and Signal Processing, MISP 2021
Country/Territory	India
City	Jote
Period	23-09-21 → 25-09-21

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering

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10.1007/978-981-19-0840-8_13

Cite this

Anagha, P., & Balasundaram, S. (2022). On Twin Bounded Support Vector Machine with Pinball Loss. In D. Gupta, K. Sambyo, M. Prasad, & S. Agarwal (Eds.), Advanced Machine Intelligence and Signal Processing (pp. 177-190). (Lecture Notes in Electrical Engineering; Vol. 858). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-0840-8_13

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title = "On Twin Bounded Support Vector Machine with Pinball Loss",

abstract = "With the introduction of pinball loss for misclassification, we present a novel pinball loss-based twin bounded support vector machine (Pin-TBSVM) having regularization term, scatter and misclassification loss term to enhance noise robustness. Pin-TBSVM is a non-parallel classifier where two kernel generated surfaces are constructed by solving quadratic programming problems. Experimental results on fourteen benchmark datasets show that the proposed method achieves improved accuracy performance than the popular traditional methods considered for comparison clearly indicating the advantage of the proposed method.",

author = "P. Anagha and S. Balasundaram",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 3rd International Conference on Machine Intelligence and Signal Processing, MISP 2021 ; Conference date: 23-09-2021 Through 25-09-2021",

year = "2022",

doi = "10.1007/978-981-19-0840-8\_13",

language = "English",

isbn = "9789811908392",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

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booktitle = "Advanced Machine Intelligence and Signal Processing",

address = "Germany",

}

Anagha, P & Balasundaram, S 2022, On Twin Bounded Support Vector Machine with Pinball Loss. in D Gupta, K Sambyo, M Prasad & S Agarwal (eds), Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol. 858, Springer Science and Business Media Deutschland GmbH, pp. 177-190, 3rd International Conference on Machine Intelligence and Signal Processing, MISP 2021, Jote, India, 23-09-21. https://doi.org/10.1007/978-981-19-0840-8_13

On Twin Bounded Support Vector Machine with Pinball Loss. / Anagha, P.; Balasundaram, S.
Advanced Machine Intelligence and Signal Processing. ed. / Deepak Gupta; Koj Sambyo; Mukesh Prasad; Sonali Agarwal. Springer Science and Business Media Deutschland GmbH, 2022. p. 177-190 (Lecture Notes in Electrical Engineering; Vol. 858).

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

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T1 - On Twin Bounded Support Vector Machine with Pinball Loss

AU - Anagha, P.

AU - Balasundaram, S.

PY - 2022

Y1 - 2022

N2 - With the introduction of pinball loss for misclassification, we present a novel pinball loss-based twin bounded support vector machine (Pin-TBSVM) having regularization term, scatter and misclassification loss term to enhance noise robustness. Pin-TBSVM is a non-parallel classifier where two kernel generated surfaces are constructed by solving quadratic programming problems. Experimental results on fourteen benchmark datasets show that the proposed method achieves improved accuracy performance than the popular traditional methods considered for comparison clearly indicating the advantage of the proposed method.

AB - With the introduction of pinball loss for misclassification, we present a novel pinball loss-based twin bounded support vector machine (Pin-TBSVM) having regularization term, scatter and misclassification loss term to enhance noise robustness. Pin-TBSVM is a non-parallel classifier where two kernel generated surfaces are constructed by solving quadratic programming problems. Experimental results on fourteen benchmark datasets show that the proposed method achieves improved accuracy performance than the popular traditional methods considered for comparison clearly indicating the advantage of the proposed method.

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DO - 10.1007/978-981-19-0840-8_13

M3 - Conference contribution

AN - SCOPUS:85134326534

SN - 9789811908392

T3 - Lecture Notes in Electrical Engineering

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BT - Advanced Machine Intelligence and Signal Processing

A2 - Gupta, Deepak

A2 - Sambyo, Koj

A2 - Prasad, Mukesh

A2 - Agarwal, Sonali

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd International Conference on Machine Intelligence and Signal Processing, MISP 2021

Y2 - 23 September 2021 through 25 September 2021

ER -

On Twin Bounded Support Vector Machine with Pinball Loss

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