A Transductive Support Vector Machine with adjustable quasi-linear kernel for semi-supervised data classification

Bo Zhou, Chenlong Hu, Benhui Chen, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper focuses on semi-supervised classification problem by using Transductive Support Vector Machine. Traditional TSVM for semi-supervised classification firstly train an SVM model with labeled data. Then use the model to predict unlabeled data and optimize unlabeled data prediction to retrain the SVM. TSVM always uses a predefined kernel and fixed parameters during the optimization procedure and they also suffers potential over-fitting problem. In this paper we introduce proposed quasi-linear kernel to the TSVM. An SVM with quasi-linear kernel realizes an approximate nonlinear separation boundary by multi-local linear boundaries with interpolation. By applying quasi-linear kernel to semi-supervised classification it can avoid potential over-fitting and provide more accurate unlabeled data prediction. After unlabeled data prediction optimization, the quasi-linear kernel can be further adjusted considering the potential boundary data distribution as prior knowledge. We also introduce a minimal set method for optimizing unlabeled data prediction. The minimal set method follows the clustering assumption of semi-supervised learning. The pairwise label switching is allowed between minimal sets. It can speed up optimization procedure and reduce influence from label constrain in TSVM. Experiment results on benchmark gene datasets show that the proposed method is effective and improves classification performances.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1409-1415
Number of pages7
ISBN (Print)9781479914845
DOIs
Publication statusPublished - 2014 Sep 3
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing
Duration: 2014 Jul 62014 Jul 11

Other

Other2014 International Joint Conference on Neural Networks, IJCNN 2014
CityBeijing
Period14/7/614/7/11

Fingerprint

Support vector machines
Labels
Supervised learning
Interpolation
Genes
Experiments

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Zhou, B., Hu, C., Chen, B., & Furuzuki, T. (2014). A Transductive Support Vector Machine with adjustable quasi-linear kernel for semi-supervised data classification. In Proceedings of the International Joint Conference on Neural Networks (pp. 1409-1415). [6889703] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889703

A Transductive Support Vector Machine with adjustable quasi-linear kernel for semi-supervised data classification. / Zhou, Bo; Hu, Chenlong; Chen, Benhui; Furuzuki, Takayuki.

Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1409-1415 6889703.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhou, B, Hu, C, Chen, B & Furuzuki, T 2014, A Transductive Support Vector Machine with adjustable quasi-linear kernel for semi-supervised data classification. in Proceedings of the International Joint Conference on Neural Networks., 6889703, Institute of Electrical and Electronics Engineers Inc., pp. 1409-1415, 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, 14/7/6. https://doi.org/10.1109/IJCNN.2014.6889703
Zhou B, Hu C, Chen B, Furuzuki T. A Transductive Support Vector Machine with adjustable quasi-linear kernel for semi-supervised data classification. In Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1409-1415. 6889703 https://doi.org/10.1109/IJCNN.2014.6889703
Zhou, Bo ; Hu, Chenlong ; Chen, Benhui ; Furuzuki, Takayuki. / A Transductive Support Vector Machine with adjustable quasi-linear kernel for semi-supervised data classification. Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1409-1415
@inproceedings{7681d79df11e4598b3b2a071cff3fb05,
title = "A Transductive Support Vector Machine with adjustable quasi-linear kernel for semi-supervised data classification",
abstract = "This paper focuses on semi-supervised classification problem by using Transductive Support Vector Machine. Traditional TSVM for semi-supervised classification firstly train an SVM model with labeled data. Then use the model to predict unlabeled data and optimize unlabeled data prediction to retrain the SVM. TSVM always uses a predefined kernel and fixed parameters during the optimization procedure and they also suffers potential over-fitting problem. In this paper we introduce proposed quasi-linear kernel to the TSVM. An SVM with quasi-linear kernel realizes an approximate nonlinear separation boundary by multi-local linear boundaries with interpolation. By applying quasi-linear kernel to semi-supervised classification it can avoid potential over-fitting and provide more accurate unlabeled data prediction. After unlabeled data prediction optimization, the quasi-linear kernel can be further adjusted considering the potential boundary data distribution as prior knowledge. We also introduce a minimal set method for optimizing unlabeled data prediction. The minimal set method follows the clustering assumption of semi-supervised learning. The pairwise label switching is allowed between minimal sets. It can speed up optimization procedure and reduce influence from label constrain in TSVM. Experiment results on benchmark gene datasets show that the proposed method is effective and improves classification performances.",
author = "Bo Zhou and Chenlong Hu and Benhui Chen and Takayuki Furuzuki",
year = "2014",
month = "9",
day = "3",
doi = "10.1109/IJCNN.2014.6889703",
language = "English",
isbn = "9781479914845",
pages = "1409--1415",
booktitle = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - A Transductive Support Vector Machine with adjustable quasi-linear kernel for semi-supervised data classification

AU - Zhou, Bo

AU - Hu, Chenlong

AU - Chen, Benhui

AU - Furuzuki, Takayuki

PY - 2014/9/3

Y1 - 2014/9/3

N2 - This paper focuses on semi-supervised classification problem by using Transductive Support Vector Machine. Traditional TSVM for semi-supervised classification firstly train an SVM model with labeled data. Then use the model to predict unlabeled data and optimize unlabeled data prediction to retrain the SVM. TSVM always uses a predefined kernel and fixed parameters during the optimization procedure and they also suffers potential over-fitting problem. In this paper we introduce proposed quasi-linear kernel to the TSVM. An SVM with quasi-linear kernel realizes an approximate nonlinear separation boundary by multi-local linear boundaries with interpolation. By applying quasi-linear kernel to semi-supervised classification it can avoid potential over-fitting and provide more accurate unlabeled data prediction. After unlabeled data prediction optimization, the quasi-linear kernel can be further adjusted considering the potential boundary data distribution as prior knowledge. We also introduce a minimal set method for optimizing unlabeled data prediction. The minimal set method follows the clustering assumption of semi-supervised learning. The pairwise label switching is allowed between minimal sets. It can speed up optimization procedure and reduce influence from label constrain in TSVM. Experiment results on benchmark gene datasets show that the proposed method is effective and improves classification performances.

AB - This paper focuses on semi-supervised classification problem by using Transductive Support Vector Machine. Traditional TSVM for semi-supervised classification firstly train an SVM model with labeled data. Then use the model to predict unlabeled data and optimize unlabeled data prediction to retrain the SVM. TSVM always uses a predefined kernel and fixed parameters during the optimization procedure and they also suffers potential over-fitting problem. In this paper we introduce proposed quasi-linear kernel to the TSVM. An SVM with quasi-linear kernel realizes an approximate nonlinear separation boundary by multi-local linear boundaries with interpolation. By applying quasi-linear kernel to semi-supervised classification it can avoid potential over-fitting and provide more accurate unlabeled data prediction. After unlabeled data prediction optimization, the quasi-linear kernel can be further adjusted considering the potential boundary data distribution as prior knowledge. We also introduce a minimal set method for optimizing unlabeled data prediction. The minimal set method follows the clustering assumption of semi-supervised learning. The pairwise label switching is allowed between minimal sets. It can speed up optimization procedure and reduce influence from label constrain in TSVM. Experiment results on benchmark gene datasets show that the proposed method is effective and improves classification performances.

UR - http://www.scopus.com/inward/record.url?scp=84908474520&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84908474520&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2014.6889703

DO - 10.1109/IJCNN.2014.6889703

M3 - Conference contribution

SN - 9781479914845

SP - 1409

EP - 1415

BT - Proceedings of the International Joint Conference on Neural Networks

PB - Institute of Electrical and Electronics Engineers Inc.

ER -