Semi-Supervised Streaming Learning with Emerging New Labels
Authors: Yong-Nan Zhu, Yu-Feng Li7015-7022
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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, φ = 128, and k is half the number of features. For SEENLP, τ = 50 and we use the standard RBF similarity to inialize the weight matrix, Wxi,xj = exp( xi xj 2/σ2), where σ can be obtained by crossvalidation from the first period. The number of labeled instances in each class in the four datasets as ordered in Table 2 is 20, 50, 80, and 50. |