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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Outlier Detection Ensemble with Embedded Feature Selection
Authors: Li Cheng, Yijie Wang, Xinwang Liu, Bin Li3503-3512
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS. and Extensive empirical results on 12 real-world data sets show that ODEFS (i) reduces a large proportion of features and improves the performance of the original bare method; (ii) performs substantially better and more stably than the state-of-the-art competitors; (iii) has much better resilience to noisy features than its competitors; (iv) has linear time complexity w.r.t. data size and feature size. |
| Researcher Affiliation | Academia | Li Cheng,1 Yijie Wang,1* Xinwang Liu, Bin Li1 1Science and Technology on Parallel and Distributed Processing Laboratory College of Computer, National University of Defense Technology Changsha, China EMAIL |
| Pseudocode | Yes | Algorithm 1 ODEFS |
| Open Source Code | No | No statement regarding the release of source code for ODEFS or a link to a code repository was found. |
| Open Datasets | Yes | They are available at http://archive.ics.uci.edu/ml/index.php, http://odds.cs.stonybrook.edu/, http://vision.cs.uiuc.edu/attributes/ |
| Dataset Splits | No | The paper mentions 'training set' but does not specify explicit train/validation/test splits (e.g., percentages, sample counts) for the datasets used in their experiments. |
| Hardware Specification | Yes | All the experiments are executed at a PC in a 3.6GHz CPU with 16GB memory. |
| Software Dependencies | Yes | ODEFS and its competitors are implemented in Python 3.4. |
| Experiment Setup | Yes | In our experiments, ODEFS uses m = 32 for small datasets (i.e., n ≤ 10^4) and m = 64 for large datasets (i.e., n > 10^4). Other parameters setting, i.e., a = 2, m' = 6m, l = 2*sqrt(n) has been explained in the above sections. The parameters of Le Si NN are set as the recommended settings. and epsilon = 0.05 is a small constant |