Adapted tree boosting for Transfer Learning

by   Wenjing Fang, et al.
Ant Financial
Shanghai Jiao Tong University

Secure online transaction is an essential task for e-commerce platforms. Alipay, one of the world's leading cashless payment platform, provides the payment service to both merchants and individual customers. The fraud detection models are built to protect the customers, but stronger demands are raised by the new scenes, which are lacking in training data and labels. The proposed model makes a difference by utilizing the data under similar old scenes and the data under a new scene is treated as the target domain to be promoted. Inspired by this real case in Alipay, we view the problem as a transfer learning problem and design a set of revise strategies to transfer the source domain models to the target domain under the framework of gradient boosting tree models. This work provides an option for the cold-starting and data-sharing problems.


page 1

page 2

page 3

page 4


VerifyTL: Secure and Verifiable Collaborative Transfer Learning

Getting access to labelled datasets in certain sensitive application dom...

Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao

As one of the largest e-commerce platforms in the world, Taobao's recomm...

Adversarially robust transfer learning

Transfer learning, in which a network is trained on one task and re-purp...

Secure Federated Transfer Learning

Machine learning relies on the availability of a vast amount of data for...

Detecting Bias in Transfer Learning Approaches for Text Classification

Classification is an essential and fundamental task in machine learning,...

Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection

With the explosive growth of the e-commerce industry, detecting online t...

Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models

The traditional paradigm for developing machine prognostics usually reli...

Please sign up or login with your details

Forgot password? Click here to reset