Summary of Evaluating the Efficacy Of Instance Incremental Vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming For Fraud Detection, by Kodjo Mawuena Amekoe et al.
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection
by Kodjo Mawuena Amekoe, Mustapha Lebbah, Gregoire Jaffre, Hanene Azzag, Zaineb Chelly Dagdia
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the effectiveness of instance incremental learning in real-world scenarios where data streams are evolving and labels may be delayed. Unlike previous studies that focus on immediate label availability, this research examines whether instance incremental learning remains the best option when labels are delayed. The authors conduct an empirical evaluation using a fraud detection problem and generated datasets, comparing state-of-the-art models like Adaptive Random Forest (ARF) with batch learning models such as XGBoost. Surprisingly, the findings suggest that instance incremental learning is not the superior choice, especially when considering interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers investigate whether instance incremental learning remains the best option when labels are delayed in real-world scenarios. They use a fraud detection problem and generated datasets to compare state-of-the-art models like Adaptive Random Forest (ARF) with batch learning models such as XGBoost. The results show that instance incremental learning is not always the best choice, especially when considering interpretability. |
Keywords
» Artificial intelligence » Random forest » Xgboost
