Triple-O for SHL Recognition Challenge: An Ensemble Framework for Multi-class Imbalance and Training-testing Distribution Inconsistency by OvO Binarization with Confidence Weight of One-class Classification-Reference-Cited by-同舟云学术

Triple-O for SHL Recognition Challenge: An Ensemble Framework for Multi-class Imbalance and Training-testing Distribution Inconsistency by OvO Binarization with Confidence Weight of One-class Classification

Published:2021-09-21 Issue: Volume: Page:
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Container-title:Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers
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Author:

Su Jinhua¹,Zhang Yuanyuan²

Affiliation:

1. Renmin University of China, China

2. Beijing Baixingkefu Network Technology Co., Ltd., China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3460418.3479375

Reference22 articles.

1. The Distance-Based Balancing Ensemble Method for Data With a High Imbalance Ratio

2. An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes

3. The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics With Mobile Devices

4. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning

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