A Framework of Online Learning with Imbalanced Streaming Data
Authors: Yan Yan, Tianbao Yang, Yi Yang, Jianhui Chen
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical studies demonstrate the competitive if not better performance of the proposed method compared to previous cost-sensitive and resampling based online learning algorithms and those that are designed for optimizing certain measures. In this section, we evaluate OMCSL for optimizing three measures, F-measure, AUROC and AUPRC, and compare with competing online learning algorithms on three public imbalanced datasets. |
| Researcher Affiliation | Collaboration | Yan Yan,1 Tianbao Yang,2 Yi Yang,1 Jianhui Chen3 1QCIS, University of Technology Sydney, 15 Broadway, Ultimo NSW 2007, Australia 2Department of Computer Science, The University of Iowa, Iowa City, IA 52242, USA 3Yahoo! Labs, Sunnyvale, CA 94089, USA |
| Pseudocode | Yes | Algorithm 1 A Framework of Online Multiple Cost-sensitive Learning |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links, explicit statements of code release) for the source code of the described methodology. |
| Open Datasets | Yes | We compare the proposed OMCSL method with several state of the art online learning algorithms... on three public imbalanced datasets. Table 2 lists the statistics of used three datasets. To construct imbalanced data from multiclass datasets covtype, we sample instances of the fifth class as positive and instances of the first class as negative, denoted by covtype1v5. Similarly, for aloi, we sample instances of the first class as positive, and the rest as negative, denoted by aloi-1. |
| Dataset Splits | No | For each dataset, we randomly sample 4/5 instances as the training set and the rest 1/5 as the testing set. The paper specifies training and testing splits but does not mention a separate validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments, only general mentions of experimentation. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., Python, PyTorch, TensorFlow versions) that would be needed to replicate the experiment. |
| Experiment Setup | No | The details of hyperparameters of these methods can be found in Appendix D. While hyperparameters are mentioned, their specific values are not provided in the main text. |