Summary of Easyfs: An Efficient Model-free Feature Selection Framework Via Elastic Transformation Of Features, by Jianming Lv et al.
EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features
by Jianming Lv, Sijun Xia, Depin Liang, Wei Chen
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel model-free feature selection framework, dubbed EasyFS, is proposed to improve upon traditional methods that neglect interfeature relationships. Unlike model-aware approaches, EasyFS leverages elastic expansion and compression to combine features non-linearly and discover correlated patterns. A novel redundancy measurement is also introduced to efficiently filter out redundant features. Experimental results on 21 datasets demonstrate EasyFS’s superiority over state-of-the-art methods in regression (up to 10.9%) and classification tasks (up to 5.7%), while achieving significant time savings (over 94%). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EasyFS is a new way to pick the most important features from a group without using a specific model. This is different from other methods that don’t consider how features work together. EasyFS works by stretching and compressing the features in a special way, which helps find patterns between them. It also has a new way to measure when features are too similar, so it can get rid of those that aren’t adding much. In tests on many different datasets, EasyFS did better than other methods for both regression (up to 10.9%) and classification tasks (up to 5.7%), while saving time by more than 94%. |
Keywords
* Artificial intelligence * Classification * Feature selection * Regression
