Learning with Feature Evolvable Streams
Authors: Bo-Jian Hou, Lijun Zhang, Zhi-Hua Zhou
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real data validate the effectiveness of our proposal. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China {houbj,zhanglj,zhouzh}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Initialize, Algorithm 2 FESL-c(ombination), Algorithm 3 FESL-s(election) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Datasets can be found in http://archive.ics.uci.edu/ml/. |
| Dataset Splits | No | The paper describes the temporal streaming data setup (T1, T2 periods) but does not provide specific train/validation/test dataset splits (e.g., percentages or counts) for the overall datasets used. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory, or specific computing platforms) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., libraries, frameworks, or programming language versions) used for the implementation. |
| Experiment Setup | Yes | We set almost T1 and T2 to be half of the number of instances, and τt to be 1/(c t) where c is searched in the range {1, 10, 50, 100, 150}. |