Summary of Icm Ensemble with Novel Betting Functions For Concept Drift, by Charalambos Eliades and Harris Papadopoulos
ICM Ensemble with Novel Betting Functions for Concept Drift
by Charalambos Eliades, Harris Papadopoulos
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 refined Inductive Conformal Martingale (ICM) approach, an extension to previous work, tackles Concept Drift (CD) by enhancing the CAUTIOUS betting function with multiple density estimators. The improved ICM is combined with Interpolated Histogram and Nearest Neighbor Density Estimators, and evaluated using single ICM and ensemble of ICMs on four benchmark datasets. The results show that the proposed approach outperforms previous methodology while matching or exceeding three state-of-the-art techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study makes a new way to deal with changes in data patterns over time called Concept Drift. It uses a special type of math called Inductive Conformal Martingale (ICM) and makes it better by adding different ways to understand how the data is spread out. The ICM is tested on its own and with other ICMs working together, using four sets of practice data. The results show that this new way works better than what came before. |
Keywords
» Artificial intelligence » Nearest neighbor
