Summary of Evolutionary Multi-objective Optimisation For Fairness-aware Self Adjusting Memory Classifiers in Data Streams, by Pivithuru Thejan Amarasinghe et al.
Evolutionary Multi-Objective Optimisation for Fairness-Aware Self Adjusting Memory Classifiers in Data Streams
by Pivithuru Thejan Amarasinghe, Diem Pham, Binh Tran, Su Nguyen, Yuan Sun, Damminda Alahakoon
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 The novel approach introduced in this paper combines self-adjusting memory K-Nearest-Neighbour algorithms with evolutionary multi-objective optimization to enhance fairness in machine learning algorithms applied to data stream classification. This approach aims to ensure fair treatment of individuals across sensitive attributes like race or gender, which is critical in dynamic data stream environments. The proposed method efficiently manages concept drift in streaming data and leverages the flexibility of evolutionary multi-objective optimization to maximize accuracy and minimize discrimination simultaneously. The effectiveness of this approach is demonstrated through extensive experiments on various datasets, comparing its performance against several baseline methods in terms of accuracy and fairness metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make sure machine learning algorithms are fair when classifying data that’s coming in real-time. It does this by combining two existing techniques: self-adjusting memory K-Nearest-Neighbour algorithms and evolutionary multi-objective optimization. This combination helps the algorithm stay accurate even as the data changes, while also making sure it doesn’t discriminate against certain groups of people. The results show that this approach is good at both being accurate and being fair, making it a promising solution for classifying streaming data. |
Keywords
» Artificial intelligence » Classification » Machine learning » Optimization
