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..
Semi-Supervised Streaming Learning with Emerging New Labels
Authors: Yong-Nan Zhu, Yu-Feng Li7015-7022
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on several datasets validate that the algorithm can accurately classify points on a dynamic stream with a small number of labeled examples and emerging new classes. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | Algorithm 1 summarizes the approach. Algorithm 2 summarizes the construction of SEENTree. SEENLP is presented in Algorithm 3. |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code availability. |
| Open Datasets | Yes | To evaluate the predictive performance of the proposed SEEN approach, we use four multi-class benchmark datasets ( segment , satimage , usps , pendigits , detailed information is shown in Table 2)1. 1https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html |
| Dataset Splits | No | The paper describes how the data stream is simulated, with new classes emerging over time, but it does not specify traditional train/validation/test dataset splits with exact percentages or sample counts for static partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions algorithms used (e.g., SVM, K-means) and their settings, but it does not specify software dependencies with version numbers (e.g., 'Python 3.x', 'PyTorch 1.x'). |
| Experiment Setup | Yes | For SEENForest, hm = 7, s = 50, Ο = 128, and k is half the number of features. For SEENLP, Ο = 50 and we use the standard RBF similarity to inialize the weight matrix, Wxi,xj = exp( xi xj 2/Ο2), where Ο can be obtained by crossvalidation from the ο¬rst period. The number of labeled instances in each class in the four datasets as ordered in Table 2 is 20, 50, 80, and 50. |