Prediction of bank telemarketing with co-training of mixture-of-experts and MLP

Jae Min Yu; Sung Bae Cho

doi:10.1007/978-3-319-46681-1_7

Prediction of bank telemarketing with co-training of mixture-of-experts and MLP

Jae Min Yu, Sung Bae Cho

College of Computing

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

2 Citations (Scopus)

Abstract

Utilization of financial data becomes one of the important issues for user adaptive marketing on the bank service. The marketing is conducted based on personal information with various facts that affect a success (clients agree to accept financial instrument). Personal information can be collected continuously anytime if clients want to agree to use own information in case of opening an account in bank, but labeling all the data needs to pay a high cost. In this paper, focusing on this characteristics of financial data, we present a global-local co-training (GLCT) algorithm to utilize labeled and unlabeled data to construct better prediction model. We performed experiments using real-world data from Portuguese bank. Experiments show that GLCT performs well regardless of the ratio of initial labeled data. Through the series of iterating experiments, we obtained better results on various aspects.

Original language	English
Title of host publication	Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
Editors	Kazushi Ikeda, Minho Lee, Akira Hirose, Seiichi Ozawa, Kenji Doya, Derong Liu
Publisher	Springer Verlag
Pages	52-59
Number of pages	8
ISBN (Print)	9783319466804
DOIs	https://doi.org/10.1007/978-3-319-46681-1_7
Publication status	Published - 2016
Event	23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan Duration: 2016 Oct 16 → 2016 Oct 21

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9950 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	23rd International Conference on Neural Information Processing, ICONIP 2016
Country/Territory	Japan
City	Kyoto
Period	16/10/16 → 16/10/21

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-46681-1_7

Cite this

Yu, J. M., & Cho, S. B. (2016). Prediction of bank telemarketing with co-training of mixture-of-experts and MLP. In K. Ikeda, M. Lee, A. Hirose, S. Ozawa, K. Doya, & D. Liu (Eds.), Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings (pp. 52-59). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9950 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46681-1_7

Yu, Jae Min ; Cho, Sung Bae. / Prediction of bank telemarketing with co-training of mixture-of-experts and MLP. Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. editor / Kazushi Ikeda ; Minho Lee ; Akira Hirose ; Seiichi Ozawa ; Kenji Doya ; Derong Liu. Springer Verlag, 2016. pp. 52-59 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e59ee0bd1d2b47ebb93828fda13d49c7,

title = "Prediction of bank telemarketing with co-training of mixture-of-experts and MLP",

abstract = "Utilization of financial data becomes one of the important issues for user adaptive marketing on the bank service. The marketing is conducted based on personal information with various facts that affect a success (clients agree to accept financial instrument). Personal information can be collected continuously anytime if clients want to agree to use own information in case of opening an account in bank, but labeling all the data needs to pay a high cost. In this paper, focusing on this characteristics of financial data, we present a global-local co-training (GLCT) algorithm to utilize labeled and unlabeled data to construct better prediction model. We performed experiments using real-world data from Portuguese bank. Experiments show that GLCT performs well regardless of the ratio of initial labeled data. Through the series of iterating experiments, we obtained better results on various aspects.",

author = "Yu, {Jae Min} and Cho, {Sung Bae}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 23rd International Conference on Neural Information Processing, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46681-1_7",

language = "English",

isbn = "9783319466804",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "52--59",

editor = "Kazushi Ikeda and Minho Lee and Akira Hirose and Seiichi Ozawa and Kenji Doya and Derong Liu",

booktitle = "Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings",

address = "Germany",

}

Yu, JM & Cho, SB 2016, Prediction of bank telemarketing with co-training of mixture-of-experts and MLP. in K Ikeda, M Lee, A Hirose, S Ozawa, K Doya & D Liu (eds), Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9950 LNCS, Springer Verlag, pp. 52-59, 23rd International Conference on Neural Information Processing, ICONIP 2016, Kyoto, Japan, 16/10/16. https://doi.org/10.1007/978-3-319-46681-1_7

Prediction of bank telemarketing with co-training of mixture-of-experts and MLP. / Yu, Jae Min; Cho, Sung Bae.
Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. ed. / Kazushi Ikeda; Minho Lee; Akira Hirose; Seiichi Ozawa; Kenji Doya; Derong Liu. Springer Verlag, 2016. p. 52-59 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9950 LNCS).

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

TY - GEN

T1 - Prediction of bank telemarketing with co-training of mixture-of-experts and MLP

AU - Yu, Jae Min

AU - Cho, Sung Bae

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Utilization of financial data becomes one of the important issues for user adaptive marketing on the bank service. The marketing is conducted based on personal information with various facts that affect a success (clients agree to accept financial instrument). Personal information can be collected continuously anytime if clients want to agree to use own information in case of opening an account in bank, but labeling all the data needs to pay a high cost. In this paper, focusing on this characteristics of financial data, we present a global-local co-training (GLCT) algorithm to utilize labeled and unlabeled data to construct better prediction model. We performed experiments using real-world data from Portuguese bank. Experiments show that GLCT performs well regardless of the ratio of initial labeled data. Through the series of iterating experiments, we obtained better results on various aspects.

AB - Utilization of financial data becomes one of the important issues for user adaptive marketing on the bank service. The marketing is conducted based on personal information with various facts that affect a success (clients agree to accept financial instrument). Personal information can be collected continuously anytime if clients want to agree to use own information in case of opening an account in bank, but labeling all the data needs to pay a high cost. In this paper, focusing on this characteristics of financial data, we present a global-local co-training (GLCT) algorithm to utilize labeled and unlabeled data to construct better prediction model. We performed experiments using real-world data from Portuguese bank. Experiments show that GLCT performs well regardless of the ratio of initial labeled data. Through the series of iterating experiments, we obtained better results on various aspects.

UR - http://www.scopus.com/inward/record.url?scp=84992761611&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84992761611&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46681-1_7

DO - 10.1007/978-3-319-46681-1_7

M3 - Conference contribution

AN - SCOPUS:84992761611

SN - 9783319466804

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

SP - 52

EP - 59

BT - Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings

A2 - Ikeda, Kazushi

A2 - Lee, Minho

A2 - Hirose, Akira

A2 - Ozawa, Seiichi

A2 - Doya, Kenji

A2 - Liu, Derong

PB - Springer Verlag

T2 - 23rd International Conference on Neural Information Processing, ICONIP 2016

Y2 - 16 October 2016 through 21 October 2016

ER -

Yu JM, Cho SB. Prediction of bank telemarketing with co-training of mixture-of-experts and MLP. In Ikeda K, Lee M, Hirose A, Ozawa S, Doya K, Liu D, editors, Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. Springer Verlag. 2016. p. 52-59. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46681-1_7

Prediction of bank telemarketing with co-training of mixture-of-experts and MLP

Abstract

Publication series

Other

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this