Authors: Cheng Zhen, Nischal Aryal, Arash Termehchy, Amandeep Singh Chabada

Published on: February 27, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2402.17926

Summary

• What is new: The approach for determining the necessity of data imputation directly and efficiently in training accurate models.
• Why this is important: Real-world data often has missing values, making it challenging to train accurate models without significant imputation efforts.
• What the research proposes: A unified approach and efficient algorithms to check the need for data imputation for various machine learning paradigms, potentially bypassing it.
• Results: Significant reduction in time and effort needed for data imputation, without considerable computational overhead.

Technical Details

Technological frameworks used: Unified approach for assessing the necessity of data imputation

Models used: Various widely-used machine learning paradigms

Data used: Real-world datasets with missing values

Potential Impact

Sectors relying on large-scale data analysis and machine learning models, such as healthcare, finance, and e-commerce.

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