Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels
Authors: Shujian Yu, Xiaoyang Wang, José C. Príncipe
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |