Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels
Authors: Shujian Yu, Xiaoyang Wang, José C. Príncipe
IJCAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,708 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,847 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Two sets of experiments are performed to evaluate the performance of HHT-CU and HHT-AG. First, quantitative metrics and plots are presented to demonstrate HHT-CU and HHTAG s effectiveness and superiority over state-of-the-art approaches on benchmark synthetic data. Then, we validate, via three real-world applications, the effectiveness of the proposed HHT-CU and HHT-AG on streaming data classification and the accuracy of its detected concept drift points. |
| Researcher Affiliation | Collaboration | 1 Nokia Bell Labs, Murray Hill, NJ, USA 2 Dept. of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA |
| Pseudocode | Yes | Algorithm 1 HHT with Classification Uncertainty (HHT-CU) and Algorithm 2 HHT with Attribute-wise Goodness of fit (HHT-AG) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | Eight datasets are selected from [Souza et al., 2015; Dyer et al., 2014], namely 2CDT, 2CHT, UG-2C-2D, MG-2C-2D, 4CR, 4CRE-V1, 4CE1CF, 5CVT. Three widely used real-world datasets are selected, namely USENET1 [Katakis et al., 2008], Keystroke [Souza et al., 2015] and Posture [Kaluˇza et al., 2010]. |
| Dataset Splits | No | The paper describes a streaming data setup with a sliding window approach and initial classifier training, but does not specify explicit training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'soft margin SVM as the baseline classifier' but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We use the parameters recommended in the papers for each competing method. The detailed values on significance levels or thresholds (if there exist) are shown in Table 1. |