Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
MUVIR: Multi-View Rare Category Detection
Authors: Dawei Zhou, Jingrui He, K. Seluk Candan, Hasan Davulcu
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real data sets demonstrate the effectiveness of the proposed framework, especially in the presence of irrelevant views. |
| Researcher Affiliation | Academia | Dawei Zhou, Jingrui He, K. Seluk Candan, Hasan Davulcu Arizona State University Tempe, Arizona EMAIL |
| Pseudocode | Yes | Algorithm 1 MUVIR Algorithm; Algorithm 2 MUVIR-LI Algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | In this section, we will present the results of our algorithm on both synthetic data sets and real data sets in multiple special scenarios... Adult data set contains 48842 instances and 14 features of each example... Statlog contains 58000 examples and 7 classes. |
| Dataset Splits | No | The paper discusses dataset sizes and modifications (e.g., downsampling minority class) but does not specify train/validation/test splits, sample counts for each, or cross-validation methods. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'kernel density estimation' and refers to existing techniques like GRADE and NNDM but does not specify any software names with version numbers for implementation or dependencies. |
| Experiment Setup | Yes | In Section 4, we study the impact of the parameter d on the performance of MUVIR, and show that in general, d (0, 1.5] will lead to reasonable performance... In this experiment, we will focus on analyzing the impact from degree d and upper bound prior p. |