Dual Active Learning for Both Model and Data Selection
Authors: Ying-Peng Tang, Sheng-Jun Huang
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on 12 open ML datasets. The results demonstrate the proposed method can effectively learn a superior model with less labeled examples. |
| Researcher Affiliation | Academia | College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China {tangyp, huangsj}@nuaa.edu.cn |
| Pseudocode | Yes | Algorithm 1 The DUAL Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its own source code or a link to a code repository. |
| Open Datasets | Yes | We conduct our experiments on 12 open ML [Vanschoren et al., 2013] multiclass classification datasets. |
| Dataset Splits | Yes | Ltr, Lval split(L) Split labeled data into training set Ltr and validation set Lval. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using 'auto-sklearn' and 'scikit-learn' but does not specify their version numbers or other software dependencies with specific versions. |
| Experiment Setup | Yes | For each case, we randomly sample 40% data as test set, 5% data as initially labeled set, and the rest is treated as the unlabeled pool. The data split is repeated for 10 times with different random seeds. For the initial candidate algorithms in CASH module, we employ 12 commonly used models: Adaboost, Random Forest, Libsvm SVC, SGD, Extra Trees, Decision Tree, K Nearest Neighbors, Passive Aggressive, Gradient Boosting, LDA, QDA, Multi Layer Perceptron. We set the truncated threshold as τ = m, where m is the number of validation data. For the tradeoff β, we set the parameter empirically as β = 1/g |