Multi-Label Active Learning: Query Type Matters
Authors: Sheng-Jun Huang, Songcan Chen, Zhi-Hua Zhou
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on 20 benchmark datasets and a manually labeled real data validate that our approach not only achieves superior performance on classification, but also provides accurate ranking for relevant labels. |
| Researcher Affiliation | Academia | Sheng-Jun Huang1,3 and Songcan Chen1,3 and Zhi-Hua Zhou2,3 1College of Computer Science & Technology, Nanjing University of Aeronautics & Astronautics 2National Key Laboratory for Novel Software Technology, Nanjing University 3Collaborative Innovation Center of Novel Software Technology and Industrialization {huangsj, s.chen}@nuaa.edu.cn zhouzh@lamda.nju.edu.cn |
| Pseudocode | No | The paper describes the algorithm steps in paragraph form but does not include a formal pseudocode block or figure. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | Yes | Experiments on 20 benchmark datasets and a manually labeled real data validate that our approach... The statistical information of these datasets are summarized in Table 1... MSRA is a multi-label dataset for image classification, and consists of 1868 images with 19 candidate labels. |
| Dataset Splits | Yes | For each experiment, we randomly divide the dataset into three parts: the test set with 50% examples, the initial labeled set with 5% examples and the unlabeled pool with the rest instances. Parameters are selected via leave-one-out cross validation on the initial set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'the multi-label learning algorithm proposed in the AUDI work [Huang and Zhou, 2013]' but does not specify any software names with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | Parameters are selected via leave-one-out cross validation on the initial set... The algorithm iteratively samples data and employs stochastic gradient descent (SGD) to minimize the ranking error. At the t-th iteration of SGD, assuming the sampled triplet is (x, y, y), the model parameters can be updated according to: W t+1 0 = W t 0 γt i (wt yx wt yx ) (5) wt+1 y = wt y + γt i W t 0x (6) wt+1 y = wt y γt i W t 0x (7) where γt is the step size... After every 5 m queries, we evaluate the performance of the classification model on the test set. |