Online Learning from Data Streams with Varying Feature Spaces
Authors: Ege Beyazit, Jeevithan Alagurajah, Xindong Wu3232-3239
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on 10 datasets with varying feature spaces have been conducted to demonstrate the performance of the proposed OLVF algorithm. Moreover, experiments with trapezoidal data streams on the same datasets have been conducted to show that OLVF performs better than the state-of-the-art learning algorithm (Zhang et al. 2016). |
| Researcher Affiliation | Academia | Ege Beyazit University of Louisiana at Lafayette Lafayette, LA, USA exb6143@louisiana.edu Jeevithan Alagurajah University of Louisiana at Lafayette Lafayette, LA, USA jxa4540@louisiana.edu Xindong Wu University of Louisiana at Lafayette Lafayette, LA, USA xwu@louisiana.edu |
| Pseudocode | Yes | Algorithm 1: The OLVF Algorithm |
| 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 | We use 9 different UCI datasets to simulate these scenarios. Additionally, we demonstrate the effectiveness of the proposed sparse strategy. Finally we evaluate the performance of OLVF using the real-world dataset IMDB movie reviews (Maas et al. 2011). |
| Dataset Splits | No | The paper mentions 'average prediction accuracy on 20 random permutations of each dataset' but does not specify exact percentages, sample counts, or a formal cross-validation setup for training/validation/test splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | The parameters C and C are chosen using grid search. We set the C to 0.1, and C to 10 5. For both algorithms, we set B s to 0.1 and find their best setting for the C and C parameters by using grid search. |