Cost-Effective Active Learning from Diverse Labelers
Authors: Sheng-Jun Huang, Jia-Lve Chen, Xin Mu, Zhi-Hua Zhou
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both UCI and real crowdsourcing data sets demonstrate the superiority of our proposed approach on selecting cost-effective queries. |
| Researcher Affiliation | Academia | Sheng-Jun Huang1,3, Jia-Lve Chen2,3, Xin Mu2,3 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@nuaa.edu.cn {chenjl, mux, zhouzh}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 The CEAL Algorithm |
| Open Source Code | No | No explicit statement or link regarding open-source code for the methodology was found. |
| Open Datasets | Yes | We first perform the experimental study on 12 data sets from the University of California-Irvine (UCI) repository [Bache and Lichman, 2013]: austra, german, krvskp, spambase, splice, titato, vehicle and ringnorm. |
| Dataset Splits | Yes | For each data set, 5% of the examples are sampled to initialize the labeled set L, 30% examples are hold out as the test set for evaluating the classification model at each iteration, and the rest 65% data are taken as the pool of unlabeled data for active selection. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, etc.) were mentioned for running experiments. |
| Software Dependencies | No | We also evaluate the performance on test data by the logistic regression model implemented with LIBLINEAR [Fan et al., 2008] with default parameters. |
| Experiment Setup | No | We also evaluate the performance on test data by the logistic regression model implemented with LIBLINEAR [Fan et al., 2008] with default parameters. |