Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting
Authors: Oscar Beijbom, Mohammad Saberian, David Kriegman, Nuno Vasconcelos
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate (1) the importance of guess-aversion and (2) that the GLL loss function outperforms other loss functions for multiclass boosting. |
| Researcher Affiliation | Academia | University of California, San Diego, 9500 Gilman Drive, 92093 La Jolla, CA |
| Pseudocode | Yes | Algorithm 1 (GLL, GEL, Ls, Lt)-MCBoost |
| Open Source Code | Yes | The MATLAB implementation of the proposed boosting algorithms, along with experimental details is available in supplementary material1. |
| Open Datasets | Yes | These experiments used 10 UCI datasets and a large scale computer vision dataset for coral classification (Beijbom et al., 2012). |
| Dataset Splits | Yes | For these, training/testing partition are either predefined or the data is randomly split into 80% training and 20% testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'MATLAB implementation' and 'LIBLINEAR implementation (Fan et al., 2008)' but does not provide specific version numbers for these software components or any other libraries. |
| Experiment Setup | Yes | For each dataset, a random symmetric cost matrix was generated, Cj,k j = k drawn uniformly from [1, 10] R, and all boosted classifiers were trained with 100 iterations. The procedure was repeated 50 times per dataset... The boosting methods were trained with 500 iterations. |