Active Learning from Crowds with Unsure Option
Authors: Jinhong Zhong, Ke Tang, Zhi-Hua Zhou
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental studies on simulated and real crowdsourcing data show that, by exploiting the unsure option, ALCU-SVM achieves very promising performance. |
| Researcher Affiliation | Academia | 1UBRI, School of Computer Science and Technology University of Science and Technology of China, Hefei 230027, China 2National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | Algorithm 1: ALCU-SVM |
| Open Source Code | Yes | 1http://staff.ustc.edu.cn/ ketang/codes/IJCAI15ALCU.html |
| Open Datasets | Yes | Empirical studies on three UCI data sets (including Pima, Heart and Ionosphere) and a real crowdsourcing data set have been conducted. ... 2https://wiki.cites.illinois.edu/wiki/display/forward/Dataset UDI-Twitter Crawl-Aug2012 [Li et al., 2012] |
| Dataset Splits | Yes | Each data set was randomly divided into three parts: initial set, active learning set and testing set. To be specific, the three data sets were divided as: Pima (20,548,200); Heart (20,150,100) and Ionosphere (20,180,151), where the three elements in the parenthesis are the numbers of instances in initial set, active learning set, and testing set respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The text body of each tweet was pre-processed and transferred into a TF-IDF vector by the natural language toolkit (NLTK3)... |
| Experiment Setup | Yes | If not specified explicitly, the linear kernel was employed in the classifier (K(x, y) = x y) and the RBF kernel was in reliability models (Kt(x, y) = exp( ||x y||2)). |