Risk Minimization in the Presence of Label Noise
Authors: Wei Gao, Lu Wang, Yu-Feng li, Zhi-Hua Zhou
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the LICS algorithm is justified both theoretically and empirically. ... We evaluate the performance of the LICS algorithm on six UCI datasets: australian, breast, diabetes, german, heart and splice. ... The performance is evaluated by five trials of 5-fold cross validation, and the test accuracies are obtained by averaging over these 25 runs, as summarized in Table 1. |
| Researcher Affiliation | Academia | Wei Gao and Lu Wang and Yu-Feng Li and Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023, China {gaow, wangl, liyf, zhouzh}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Median-of-means estimator of label mean... Algorithm 2 The Labeled Instance Centroid Smooth (LICS) algorithm |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for the source code of the methodology described. |
| Open Datasets | Yes | We evaluate the performance of the LICS algorithm on six UCI1 datasets: australian, breast, diabetes, german, heart and splice. 1http://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | Yes | The performance is evaluated by five trials of 5-fold cross validation, and the test accuracies are obtained by averaging over these 25 runs, as summarized in Table 1. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | In the proposed LICS algorithm, five-fold cross-validation is executed to select the regularized parameter nλ {2 12, 2 11, . . . , 22} (n is size of training data), approximation parameter nβ {2 12, 2 12, . . . , 212}, noise rate η {0.1, 0.2, 0.3, 0.4}, and we set group number k = 3 in Algorithm 1. The parameters in all compared methods are chosen by cross-validation in a similar manner. |