Summary of Mel: Efficient Multi-task Evolutionary Learning For High-dimensional Feature Selection, by Xubin Wang et al.
MEL: Efficient Multi-Task Evolutionary Learning for High-Dimensional Feature Selection
by Xubin Wang, Haojiong Shangguan, Fengyi Huang, Shangrui Wu, Weijia Jia
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a novel approach called PSO-based Multi-task Evolutionary Learning (MEL) to tackle the “curse of dimensionality” in feature selection for data mining. MEL leverages multi-task learning to share information between different feature selection tasks, enhancing its learning ability and efficiency. The authors evaluate MEL’s effectiveness on 22 high-dimensional datasets, comparing it with 24 existing evolutionary computation (EC) approaches. The results show that MEL exhibits strong competitiveness against these EC methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn better from big data by reducing its size and making it easier to understand. It’s called the “curse of dimensionality” because as data gets bigger, it becomes harder for computers to process. The authors created a new way to solve this problem using an evolutionary learning method that can share information between different tasks. They tested their approach on 22 large datasets and found it worked well compared to other methods. |
Keywords
* Artificial intelligence * Feature selection * Multi task
