Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Active Learning from Crowds with Unsure Option
Authors: Jinhong Zhong, Ke Tang, Zhi-Hua Zhou
IJCAI 2015 | Venue PDF | 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)). |