Summary of Fair Streaming Feature Selection, by Zhangling Duan et al.
Fair Streaming Feature Selection
by Zhangling Duan, Tianci Li, Xingyu Wu, Zhaolong Ling, Jingye Yang, Zhaohong Jia
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 algorithm for fair streaming feature selection, FairSFS, is proposed to address the issue of bias and discrimination in current algorithms. The approach dynamically adjusts the feature set and discerns correlations between classification attributes and sensitive attributes, thereby preventing the propagation of sensitive data. This ensures fairness in the feature selection process without compromising accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FairSFS is designed for real-time data streams, identifying the most relevant attributes while maintaining fairness. By adapting to incoming feature vectors, it avoids perpetuating biases and discrimination. The algorithm achieves parity with leading streaming feature selection methods and existing fair feature techniques in terms of accuracy, while significantly improving fairness metrics. |
Keywords
* Artificial intelligence * Classification * Feature selection
