Conditional Bernoulli Mixtures for Multi-label Classification
Authors: Cheng Li, Bingyu Wang, Virgil Pavlu, Javed Aslam
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the effectiveness of the proposed method against competitive alternatives on benchmark datasets. We perform experiments on five commonly used and relatively large multi-label datasets: SCENE, TMC2007, MEDIAMILL, NUS-WIDE from Mulan1 and RCV1 (topics subset 1) from LIBSVM2. |
| Researcher Affiliation | Academia | Cheng Li CHENGLI@CCS.NEU.EDU Bingyu Wang RAINICY@CCS.NEU.EDU Virgil Pavlu VIP@CCS.NEU.EDU Javed Aslam JAA@CCS.NEU.EDU College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA |
| Pseudocode | Yes | Algorithm 1 Generic Training for CBM and Algorithm 2 Prediction by Dynamic Prog. and Pruning |
| Open Source Code | Yes | Our implementations of CBM and several baselines (Pow Set, PCC, CRF, etc.) are available at https://github.com/ cheng-li/pyramid. |
| Open Datasets | Yes | We perform experiments on five commonly used and relatively large multi-label datasets: SCENE, TMC2007, MEDIAMILL, NUS-WIDE from Mulan1 and RCV1 (topics subset 1) from LIBSVM2. 1http://mulan.sourceforge.net 2https://www.csie.ntu.edu.tw/~cjlin/ libsvmtools/datasets/multilabel.html |
| Dataset Splits | Yes | For the sake of reproducibility, we adopt the train/test splits provided by Mulan and LIBSVM. Hyper parameter tuning is done by cross-validation on the training set (see the supplementary material for details). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for the experiments. |
| Software Dependencies | No | The paper mentions software like logistic regressions, gradient boosted trees, L-BFGS, MEKA, LIBSVM, and Python, but does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | To avoid over-fitting, we also add L2 regularizations (Gaussian priors) to all parameters. Hyper parameter tuning is done by cross-validation on the training set (see the supplementary material for details). |