Saved in:
| Main Authors: | , |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2025
|
| Online Access: | https://doi.org/10.5281/zenodo.17818489 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- This paper explores the transformative potential of AI-powered data cleaning, focusing on the application of generative models to enhance data quality and foster trust in data-driven decision-making. Data cleaning, a crucial yet often overlooked step in data analysis, involves identifying and correcting inaccuracies, inconsistencies, and redundancies in datasets. Traditional methods are often manual, time-consuming, and prone to subjective biases. We propose a novel framework leveraging generative adversarial networks (GANs) and variational autoencoders (VAEs) to automatically detect and rectify data errors. The framework is evaluated on benchmark datasets, demonstrating significant improvements in data accuracy, completeness, and consistency compared to conventional techniques. Furthermore, we address the ethical implications of AI-driven data cleaning, emphasizing the importance of transparency and fairness in algorithm design. Our findings suggest that generative models can revolutionize data cleaning processes, leading to higher-quality data, improved analytical outcomes, and greater confidence in AI-driven insights.