Online Active Learning of Reject Option Classifiers
Authors: Kulin Shah, Naresh Manwani5652-5659
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide extensive experimental results to show the effectiveness of the proposed algorithms. The proposed algorithms efficiently reduce the number of label examples required. |
| Researcher Affiliation | Academia | Kulin Shah, Naresh Manwani Machine Learning Lab, KCIS, IIIT Hyderabad, India kulin.shah@students.iiit.ac.in, naresh.manwani@iiit.ac.in |
| Pseudocode | Yes | Algorithm 1 Double Ramp Loss Active Learning (DRAL)... Algorithm 2 Double Sigmoid Loss Active Learning (DSAL) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We show the effectiveness of the proposed active learning approaches on Gisette, Phishing and Guide datasets available on UCI ML repository (Lichman 2013). |
| Dataset Splits | No | The paper does not explicitly state training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | In all our simulations, we initialize step size by a small value, and after every trial, step size decreases by a small constant. Parameter α in the double sigmoid loss function is chosen to minimize the average risk and average fraction of queried labels (averaged over 100 runs). |