Cost-sensitive supported vector learning to rank imbalanced data set

Xiao Chang; Qinghua Zheng; Peng Lin

doi:10.1007/978-3-642-04020-7_33

Cost-sensitive supported vector learning to rank imbalanced data set

Xiao Chang
, Qinghua Zheng
, Peng Lin

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University
Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D

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

4 Scopus citations

Abstract

In recent years, the algorithms of learning to rank have been proposed by researchers. Most of these algorithms are pairwise approach. In many real world applications, instances of ranks are imbalanced. After the instances of ranks are composed to pairs, the pairs of ranks are imbalanced too. In this paper, a cost-sensitive risk minimum model of pairwise learning to rank imbalance data sets is proposed. Following this model, the algorithm of cost-sensitive supported vector learning to rank is investigated. In experiment, the convention Ranking SVM is used as baseline. The document retrieval data set is used in experiment. The experimental results show that the performance of cost-sensitive supported vector learning to rank is better than Ranking SVM on the document retrieval data set.

Original language	English
Title of host publication	Emerging Intelligent Computing Technology and Applications
Subtitle of host publication	With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings
Pages	305-314
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-04020-7_33
State	Published - 2009
Event	5th International Conference on Intelligent Computing, ICIC 2009 - Ulsan, Korea, Republic of Duration: 16 Sep 2009 → 19 Sep 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5755 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Intelligent Computing, ICIC 2009
Country/Territory	Korea, Republic of
City	Ulsan
Period	16/09/09 → 19/09/09

Keywords

Cost-sensitive learning
Imbalanced data set
Learning to rank
Supported vector learning

Access to Document

10.1007/978-3-642-04020-7_33

Cite this

Chang, X., Zheng, Q., & Lin, P. (2009). Cost-sensitive supported vector learning to rank imbalanced data set. In Emerging Intelligent Computing Technology and Applications: With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings (pp. 305-314). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5755 LNAI). https://doi.org/10.1007/978-3-642-04020-7_33

Chang, Xiao ; Zheng, Qinghua ; Lin, Peng. / Cost-sensitive supported vector learning to rank imbalanced data set. Emerging Intelligent Computing Technology and Applications: With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings. 2009. pp. 305-314 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Cost-sensitive supported vector learning to rank imbalanced data set",

abstract = "In recent years, the algorithms of learning to rank have been proposed by researchers. Most of these algorithms are pairwise approach. In many real world applications, instances of ranks are imbalanced. After the instances of ranks are composed to pairs, the pairs of ranks are imbalanced too. In this paper, a cost-sensitive risk minimum model of pairwise learning to rank imbalance data sets is proposed. Following this model, the algorithm of cost-sensitive supported vector learning to rank is investigated. In experiment, the convention Ranking SVM is used as baseline. The document retrieval data set is used in experiment. The experimental results show that the performance of cost-sensitive supported vector learning to rank is better than Ranking SVM on the document retrieval data set.",

keywords = "Cost-sensitive learning, Imbalanced data set, Learning to rank, Supported vector learning",

author = "Xiao Chang and Qinghua Zheng and Peng Lin",

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Chang, X, Zheng, Q & Lin, P 2009, Cost-sensitive supported vector learning to rank imbalanced data set. in Emerging Intelligent Computing Technology and Applications: With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5755 LNAI, pp. 305-314, 5th International Conference on Intelligent Computing, ICIC 2009, Ulsan, Korea, Republic of, 16/09/09. https://doi.org/10.1007/978-3-642-04020-7_33

Cost-sensitive supported vector learning to rank imbalanced data set. / Chang, Xiao; Zheng, Qinghua; Lin, Peng.
Emerging Intelligent Computing Technology and Applications: With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings. 2009. p. 305-314 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5755 LNAI).

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

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T1 - Cost-sensitive supported vector learning to rank imbalanced data set

AU - Chang, Xiao

AU - Zheng, Qinghua

AU - Lin, Peng

PY - 2009

Y1 - 2009

N2 - In recent years, the algorithms of learning to rank have been proposed by researchers. Most of these algorithms are pairwise approach. In many real world applications, instances of ranks are imbalanced. After the instances of ranks are composed to pairs, the pairs of ranks are imbalanced too. In this paper, a cost-sensitive risk minimum model of pairwise learning to rank imbalance data sets is proposed. Following this model, the algorithm of cost-sensitive supported vector learning to rank is investigated. In experiment, the convention Ranking SVM is used as baseline. The document retrieval data set is used in experiment. The experimental results show that the performance of cost-sensitive supported vector learning to rank is better than Ranking SVM on the document retrieval data set.

AB - In recent years, the algorithms of learning to rank have been proposed by researchers. Most of these algorithms are pairwise approach. In many real world applications, instances of ranks are imbalanced. After the instances of ranks are composed to pairs, the pairs of ranks are imbalanced too. In this paper, a cost-sensitive risk minimum model of pairwise learning to rank imbalance data sets is proposed. Following this model, the algorithm of cost-sensitive supported vector learning to rank is investigated. In experiment, the convention Ranking SVM is used as baseline. The document retrieval data set is used in experiment. The experimental results show that the performance of cost-sensitive supported vector learning to rank is better than Ranking SVM on the document retrieval data set.

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KW - Imbalanced data set

KW - Learning to rank

KW - Supported vector learning

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SN - 9783642040191

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Chang X, Zheng Q, Lin P. Cost-sensitive supported vector learning to rank imbalanced data set. In Emerging Intelligent Computing Technology and Applications: With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings. 2009. p. 305-314. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-04020-7_33

Cost-sensitive supported vector learning to rank imbalanced data set

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