# Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification
## 5 Discussion and Future Work
**Author Contributions:** Conceptualization, Q.Q. ; methodology, F.L. ; software, F.L. ; investigation, F.L. ; resources, Q.Q. ; data curation, F.L. ; writing--original draft preparation, F.L. ; writing--review and editing, Q.Q. ; supervision, Q.Q. ; project administration, Q.Q. ; funding acquisition, Q.Q. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was sponsored by the National Key Research and Development Program of China (No. 2018 YFB0704400), Key Program of Science and Technology of Yunnan Province (No. 202002 AB080001-2, 202102 AB080019-3), Key Research Project of Zhejiang Laboratory (No. 2021 PE0 AC02), Key Project of Shanghai Zhangjiang National Independent Innovation Demonstration Zone (No.ZJ2021-ZD-006). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** All of the datasets and source code that support the experimental findings can be accessed at [[https://github.com/qq-shu/cost_sensitive_VAE](https://github.com/qq-shu/cost_sensitive_VAE)]([https://github.com/qq-shu/cost_sensitive_VAE](https://github.com/qq-shu/cost_sensitive_VAE)), accessed on 7 March 2022. **Acknowledgments:** The authors gratefully appreciate the anonymous reviewers for their valuable comments. **Conflicts of Interest:** The authors declare no conflict of interest. Figure A2: Average Recall under different cost sensitive factors \(\alpha\). Figure A3: Average F1-score under different cost sensitive factors \(\alpha\). * (15) [PERSON]; [PERSON] Cluster-based under-sampling approaches for imbalanced data distributions. _Expert Syst. Appl._**2009**, _36_, 5718-5727. [CrossRef]
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