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..
Concept Drift Detection Through Resampling
Authors: Maayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the method has high recall and precision, and performs well in the presence of noise. |
| Researcher Affiliation | Academia | Maayan Harel EMAIL Koby Crammer EMAIL Ran El-Yaniv EMAIL Shie Mannor EMAIL Technion Israel Institute of Technology, Haifa, Israel. |
| Pseudocode | Yes | Algorithm 1: Concept Drift Detection Scheme; Algorithm 2: Slow Gradual Drift Detection Scheme.; Algorithm 3: TEST( ˆRord, n ˆRS i(ASi) o P i=1 , , δ) Procedure for detection scheme |
| Open Source Code | No | The paper mentions using 'scikit-learn: Machine Learning in Python toolbox' but does not provide a link or explicit statement about releasing their own implementation code. |
| Open Datasets | Yes | We compare detection performance on a user preference prediction task defined using the 20-news groups text dataset6, consisting of 18, 846 documents and over 75, 000 features. 6http://qwone.com/ jason/20Newsgroups |
| Dataset Splits | Yes | In the first step of our basic detection scheme the observed sub-sequence Zn is divided into a training window Sord = Zk 1 , 1 < k < n, and a test window S ord = Zn k+1. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory amounts used for running the experiments. |
| Software Dependencies | No | We used scikit-learn: Machine Learning in Python toolbox. Cross-validation on a single random concept showed low sensitivity to the choice of C on this dataset and therefore the default value C = 1 was chosen. The paper mentions 'scikit-learn' but does not specify its version number, nor does it specify version numbers for other key software components. |
| Experiment Setup | Yes | We set the sensitivity level of PERM and grad-PERM to δ = 0.01, = 0, the warning and detection thresholds of STEPD to w = 0.05, d = 0.01, and the parameters of EDDM to α = 0.95 and β = 0.90. The base algorithm was K-Nearest Neighbors (k = 3), each stream was randomly repeated 100 times, and P = 100 reshuffling splits were used in PERM. We use P = 500, and SVM and SVR with linear kernel as the learning algorithms. Cross-validation on a single random concept showed low sensitivity to the choice of C on this dataset and therefore the default value C = 1 was chosen. |