Convex Calibrated Surrogates for Hierarchical Classification
Authors: Harish Ramaswamy, Ambuj Tewari, Shivani Agarwal
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on a number of benchmark datasets show that the resulting algorithm, which we term Ov A-Cascade, gives improved performance over other state-of-the-art hierarchical classification algorithms. |
| Researcher Affiliation | Academia | Harish G. Ramaswamy HARISH GURUP@CSA.IISC.ERNET.IN Indian Institute of Science, Bangalore, INDIA Ambuj Tewari TEWARIA@UMICH.EDU University of Michigan, Ann Arbor, USA Shivani Agarwal SHIVANI@CSA.IISC.ERNET.IN Indian Institute of Science, Bangalore, INDIA |
| Pseudocode | Yes | Algorithm 1 OVA-Cascade Training |
| Open Source Code | No | The paper does not provide any specific repository link, explicit code release statement, or mention of code in supplementary materials for the methodology described. |
| Open Datasets | Yes | CLEF (Dimitrovski et al., 2011) Medical X-ray images organized according to a hierarchy. IPC 3 Patents organized according to the International Patent Classification Hierarchy. LSHTC-small, DMOZ-2010 and DMOZ-2012 4 Web-pages, from the LSHTC (Large-Scale Hierarchical Text Classification) challenges 2010-12, organized according to a hierarchy. 3http://www.wipo.int/classifications/ipc/en/support/ 4http://lshtc.iit.demokritos.gr/node/3 |
| Dataset Splits | Yes | Table 1. Dataset Statistics Dataset #Train #Validation #Test #Labels #Leaf-Labels Depth #Features CLEF 9,000 1,000 1,006 97 63 3 89 LSHTC-small 4,463 1,860 1,858 2,388 1,139 5 51,033 IPC 35,000 11,324 28,926 553 451 3 541,869 DMOZ-2010 80,000 13,805 34,905 17,222 12,294 5 381,580 DMOZ-2012 250,000 50,000 83,408 13,347 11,947 5 348,548 |
| Hardware Specification | No | The paper mentions running algorithms 'on a 4-core CPU' but does not provide specific details such as the CPU model, GPU, memory, or other detailed computer specifications. |
| Software Dependencies | No | The paper states 'We use LIBLINEAR (Fan et al., 2008) for the SVM-train subroutine' but does not specify the version number of this or any other software dependency. |
| Experiment Setup | No | The paper mentions that 'The regularization parameter C is chosen via a separate validation set. The thresholds τj for j [h] are also chosen via a coarse grid search using the validation set.' but does not provide the specific hyperparameter values for C or τj. |