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.
|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|
|Number of pages||8|
|Publication status||Published - 2016|
|Event||23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan|
Duration: 2016 Oct 16 → 2016 Oct 21
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||23rd International Conference on Neural Information Processing, ICONIP 2016|
|Period||16/10/16 → 16/10/21|
Bibliographical noteFunding Information:
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-R0992-15-1011) supervised by the IITP (Institute for Information & communications Technology Promotion).
© Springer International Publishing AG 2016.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)