Towards Enabling Binary Decomposition for Partial Label Learning
Authors: Xuan Wu, Min-Ling Zhang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental studies on both artificial and real-world PL data sets clearly validate the effectiveness of the proposed binary decomposition approach w.r.t state-of-the-art partial label learning techniques. |
| Researcher Affiliation | Academia | Xuan Wu1,2, Min-Ling Zhang1,2,3 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2Key Laboratory of Computer Network and Information Integration (Southeast University), MOE, China 3Collaborative Innovation Center for Wireless Communications Technology, China {wuxuan, zhangml}@seu.edu.cn |
| Pseudocode | Yes | Table 1: The pseudo-code of PALOC. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the proposed PALOC method is publicly available. |
| Open Datasets | Yes | Table 2: Characteristics of the controlled UCI data sets. (listing glass, ecoli, vehicle, abalone, usps, letter) and Table 4: Characteristic of the real-world partial label data sets (listing FG-NET [Panis and Lanitis, 2015], Lost [Cour et al., 2011], MSRCv2 [Liu and Dietterich, 2012], Bird Song [Briggs et al., 2012], Soccer Player [Zeng et al., 2013], Yahoo! News [Guillaumin et al., 2010]). |
| Dataset Splits | Yes | For each data set, ten-fold cross-validation is performed where the mean predictive accuracies and standard deviations are recorded for all comparing approaches. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions 'SVM [Chang and Lin, 2011]' as the binary base learner but does not specify its version number or any other software dependencies with version information. |
| Experiment Setup | Yes | In the rest of this paper, µ is fixed to be 10 for performance evaluation. Furthermore, similar to CLPL and PL-ECOC, SVM [Chang and Lin, 2011] is utilized to instantiate the binary base learner B for PALOC. To evaluation the performance of PALOC, five state-of-the-art partial label learning approaches with suggested parameter configurations have been employed for comparative studies: CLPL [Cour et al., 2011] which transforms partial label learning problem to binary learning problem via feature mapping with convex loss optimization [suggested configuration: SVM with squared hinge loss]; PL-KNN [H ullermeier and Beringer, 2006] which adapts k-nearest neighbor technique to learn from PL data via weighted voting [suggested configuration: k = 10]; PL-SVM [Nguyen and Caruana, 2008] which adapts maximum margin technique to learn from PL data via l2 regularization [suggested configuration: regularization parameter pool with {10 3, . . . , 103}]; LSB-CMM [Liu and Dietterich, 2012] which adapts maximum likelihood to learn from PL data via mixture models [suggested configuration: 5q mixture components]; PL-ECOC [Zhang et al., 2017] which transforms partial label learning problem to binary learning problem via ECOC coding matrix [suggested configuration: codeword length L = 10 log2(q) ]. |