Search Results for author: Zhining Liu

Found 7 papers, 4 papers with code

Adversarial Learning for Incentive Optimization in Mobile Payment Marketing

no code implementations28 Dec 2021 Xuanying Chen, Zhining Liu, Li Yu, Sen Li, Lihong Gu, Xiaodong Zeng, Yize Tan, Jinjie Gu

This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy.

Marketing

IMBENS: Ensemble Class-imbalanced Learning in Python

1 code implementation24 Nov 2021 Zhining Liu, Zhepei Wei, Erxin Yu, Qiang Huang, Kai Guo, Boyang Yu, Zhaonian Cai, Hangting Ye, Wei Cao, Jiang Bian, Pengfei Wei, Jing Jiang, Yi Chang

imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for quick implementing and deploying ensemble learning algorithms on class-imbalanced data.

Ensemble Learning

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

2 code implementations NeurIPS 2020 Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang

This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks.

imbalanced classification Meta-Learning

Self-paced Ensemble for Highly Imbalanced Massive Data Classification

1 code implementation8 Sep 2019 Zhining Liu, Wei Cao, Zhifeng Gao, Jiang Bian, Hechang Chen, Yi Chang, Tie-Yan Liu

To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers.

Classification General Classification +1

Scalable attribute-aware network embedding with locality

no code implementations17 Apr 2018 Weiyi Liu, Zhining Liu, Toyotaro Suzumura, Guangmin Hu

Here we propose \emph{SANE}, a scalable attribute-aware network embedding algorithm with locality, to learn the joint representation from topology and attributes.

Network Embedding

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