A two-phase model based on SVM and conjoint analysis for credit scoring

Kin Keung  Lai; Ligang Zhou; Lean Yu

doi:10.1007/978-3-540-72586-2_72

A two-phase model based on SVM and conjoint analysis for credit scoring

Kin Keung Lai, Ligang Zhou, Lean Yu

Department of Decision Analytics and Operations

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

In this study, we use least square support vector machines (LSSVM) to construct a credit scoring model and introduce conjoint analysis technique to analyze the relative importance of each input feature for making the decision in the model. A test based on a real-world credit dataset shows that the proposed model has good classification accuracy and can help explain the decision. Hence, it is an alternative model for credit scoring tasks. © Springer-Verlag Berlin Heidelberg 2007.

Original language	English
Title of host publication	Computational Science - ICCS 2007
Subtitle of host publication	7th International Conference, Beijing China, May 27-30, 2007, Proceedings, Part II
Editors	Yong Shi, Geert Dick Albada, Jack Dongarra, Peter M. A. Sloot
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	494-498
ISBN (Electronic)	978-3-540-72586-2
ISBN (Print)	9783540725855
DOIs	https://doi.org/10.1007/978-3-540-72586-2_72
Publication status	Published - 2007
Event	7th International Conference on Computational Science (ICCS 2007) - Beijing, China Duration: 27 May 2007 → 30 May 2007

Publication series

Name	Lecture Notes in Computer Science
Volume	4488
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Computational Science (ICCS 2007)
Place	China
City	Beijing
Period	27/05/07 → 30/05/07

Research Keywords

Conjoint analysis
Credit scoring
Support vector machines

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10.1007/978-3-540-72586-2_72

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Lai, K. K., Zhou, L., & Yu, L. (2007). A two-phase model based on SVM and conjoint analysis for credit scoring. In Y. Shi, G. D. Albada, J. Dongarra, & P. M. A. Sloot (Eds.), Computational Science - ICCS 2007: 7th International Conference, Beijing China, May 27-30, 2007, Proceedings, Part II (pp. 494-498). (Lecture Notes in Computer Science; Vol. 4488). Springer . https://doi.org/10.1007/978-3-540-72586-2_72

Lai, Kin Keung ; Zhou, Ligang ; Yu, Lean. / A two-phase model based on SVM and conjoint analysis for credit scoring. Computational Science - ICCS 2007: 7th International Conference, Beijing China, May 27-30, 2007, Proceedings, Part II. editor / Yong Shi ; Geert Dick Albada ; Jack Dongarra ; Peter M. A. Sloot. Berlin, Heidelberg : Springer , 2007. pp. 494-498 (Lecture Notes in Computer Science).

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abstract = "In this study, we use least square support vector machines (LSSVM) to construct a credit scoring model and introduce conjoint analysis technique to analyze the relative importance of each input feature for making the decision in the model. A test based on a real-world credit dataset shows that the proposed model has good classification accuracy and can help explain the decision. Hence, it is an alternative model for credit scoring tasks. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.",

keywords = "Conjoint analysis, Credit scoring, Support vector machines",

author = "Lai, {Kin Keung} and Ligang Zhou and Lean Yu",

year = "2007",

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Lai, KK, Zhou, L & Yu, L 2007, A two-phase model based on SVM and conjoint analysis for credit scoring. in Y Shi, GD Albada, J Dongarra & PMA Sloot (eds), Computational Science - ICCS 2007: 7th International Conference, Beijing China, May 27-30, 2007, Proceedings, Part II. Lecture Notes in Computer Science, vol. 4488, Springer , Berlin, Heidelberg, pp. 494-498, 7th International Conference on Computational Science (ICCS 2007), Beijing, China, 27/05/07. https://doi.org/10.1007/978-3-540-72586-2_72

A two-phase model based on SVM and conjoint analysis for credit scoring. / Lai, Kin Keung ; Zhou, Ligang; Yu, Lean.
Computational Science - ICCS 2007: 7th International Conference, Beijing China, May 27-30, 2007, Proceedings, Part II. ed. / Yong Shi; Geert Dick Albada; Jack Dongarra; Peter M. A. Sloot. Berlin, Heidelberg: Springer , 2007. p. 494-498 (Lecture Notes in Computer Science; Vol. 4488).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A two-phase model based on SVM and conjoint analysis for credit scoring

AU - Lai, Kin Keung

AU - Zhou, Ligang

AU - Yu, Lean

PY - 2007

Y1 - 2007

N2 - In this study, we use least square support vector machines (LSSVM) to construct a credit scoring model and introduce conjoint analysis technique to analyze the relative importance of each input feature for making the decision in the model. A test based on a real-world credit dataset shows that the proposed model has good classification accuracy and can help explain the decision. Hence, it is an alternative model for credit scoring tasks. © Springer-Verlag Berlin Heidelberg 2007.

AB - In this study, we use least square support vector machines (LSSVM) to construct a credit scoring model and introduce conjoint analysis technique to analyze the relative importance of each input feature for making the decision in the model. A test based on a real-world credit dataset shows that the proposed model has good classification accuracy and can help explain the decision. Hence, it is an alternative model for credit scoring tasks. © Springer-Verlag Berlin Heidelberg 2007.

KW - Conjoint analysis

KW - Credit scoring

KW - Support vector machines

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U2 - 10.1007/978-3-540-72586-2_72

DO - 10.1007/978-3-540-72586-2_72

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783540725855

T3 - Lecture Notes in Computer Science

SP - 494

EP - 498

BT - Computational Science - ICCS 2007

A2 - Shi, Yong

A2 - Albada, Geert Dick

A2 - Dongarra, Jack

A2 - Sloot, Peter M. A.

PB - Springer

CY - Berlin, Heidelberg

T2 - 7th International Conference on Computational Science (ICCS 2007)

Y2 - 27 May 2007 through 30 May 2007

ER -

Lai KK, Zhou L, Yu L. A two-phase model based on SVM and conjoint analysis for credit scoring. In Shi Y, Albada GD, Dongarra J, Sloot PMA, editors, Computational Science - ICCS 2007: 7th International Conference, Beijing China, May 27-30, 2007, Proceedings, Part II. Berlin, Heidelberg: Springer . 2007. p. 494-498. (Lecture Notes in Computer Science). Epub 2007 Jul 14. doi: 10.1007/978-3-540-72586-2_72

A two-phase model based on SVM and conjoint analysis for credit scoring

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