Summary of A Contrast Based Feature Selection Algorithm For High-dimensional Data Set in Machine Learning, by Chunxu Cao et al.
A Contrast Based Feature Selection Algorithm for High-dimensional Data set in Machine Learning
by Chunxu Cao, Qiang Zhang
First submitted to arxiv on: 15 Jan 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 This paper proposes a novel filter feature selection method called ContrastFS to address the computational bottleneck in selecting informative features from large datasets. The approach selects discriminative features based on the discrepancies between different classes, and uses a dimensionless quantity to summarize distributional individuality. This allows for efficient evaluation of features and correlation analysis. Compared to state-of-the-art methods, ContrastFS achieves favorable performance with negligible computation costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find the most important information in big data by getting rid of irrelevant details. When we apply this method to large datasets, it’s usually too slow because computers have to do a lot of work. To solve this problem, scientists created a new way to pick out good features called ContrastFS. It looks at how different groups are different from each other and uses that information to decide which features are most useful. The results show that this method works well and doesn’t take too long, making it helpful for many applications. |
Keywords
* Artificial intelligence * Feature selection
