Dr. Yilin Hu Profile

Dr. Yilin Hu

at Sichuan Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 4 August 2022 Paper

Adaptive iterative clustering and decision tree for multimodal personal credit prediction

Yi Feng, Yiji Mao, Yilin Hu

Proceedings Volume 12306, 123061K (2022) https://doi.org/10.1117/12.2641274

KEYWORDS: Data modeling, Composites, Statistical modeling, Machine learning, Process modeling, Neural networks, Visualization, Statistical analysis, Silicon, Performance modeling

Read Abstract +

With the rapid development of the personal credit market, it is accompanied by financial risks brought about by dishonesty. Among the existing personal credit prediction models, the traditional machine learning model is ineffective in dealing with multiple categories of users due to its poor generalization ability. In contrast, the neural network model requires many samples to cover multiple modal users. Its high training cost also makes this method limited. This paper proposes an adaptive iterative multimodal clustering method to predict the default of credit users. According to the multicategory characteristics of credit users, the method first divides users into multiple sets from coarse to fine-grained and then establishes decision tree models for prediction. This method not only solves the problem of the poor generalization ability of traditional machine learning models but also ensures that the overall training cost of the model is low. Comparative experiments are conducted based on representative credit datasets to verify the superiority and effectiveness of the proposed model. Experimental results show that the composite model outperforms other machine learning models and is suitable for personal credit loan prediction problems.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years