Weighted ROC Curve in Cost Space: Extending AUC to Cost-Sensitive Learning
Authors: HuiYang Shao, Qianqian Xu, Zhiyong Yang, Peisong Wen, Gao Peifeng, Qingming Huang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on multiple imbalanced cost-sensitive classification tasks. The experimental results speak to the effectiveness of our proposed methods. In this section, we conduct a series of experiments for WAUC cost-sensitive learning on common long-tail benchmark datasets. |
| Researcher Affiliation | Academia | 1 Key Lab. of Intelligent Information Processing, Institute of Computing Tech., CAS 2 School of Computer Science and Tech., University of Chinese Academy of Sciences 3 BDKM, University of Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1 Stochastic Algorithm for WAUC Cost-sensitive Learning |
| Open Source Code | Yes | The source code is available in supplemental materials. |
| Open Datasets | Yes | We use three datasets: Binary CIFAR-10-Long-Tail Dataset [23], Binary CIFAR-100-Long-Tail Dataset [23], and Jane Street Market Prediction [14]. |
| Dataset Splits | Yes | For all datasets, we divide them into the training set, validation set, and test set with a proportion 0.7:0.15:0.15. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU model, memory) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) were explicitly provided in the paper. |
| Experiment Setup | Yes | In this subsection, we show the sensitivity of β, T, and bandwidth on test data. Effect of β. Effect of T. Effect of m. |