DMCO: Budget-Aware Co-Optimization of Data Cleaning and AutoML

Data cleaning and automated machine learning (AutoML) are both crucial for reliable learning systems, yet are commonly treated as independent or sequential stages. This separation ignores their strong interaction and leads to inefficient use of limited computational budgets.We propose DMCO, a unified framework that jointly optimizes data cleaning and model construction under a fixed resource budget. DMCO reformulates the traditional two-stage pipeline into a time-sliced process, where data cleaning and AutoML are interleaved and adaptively scheduled. We introduce a gradient-based data cleaning sampling strategy with theoretical guarantees for minimizing gradient estimation variance, and integrates it with loss-driven sampling and progressive AutoML fitting to continuously leverage intermediate data quality improvements.Experiments on six real-world datasets show that DMCO consistently outperforms standalone data cleaning and AutoML baselines on both classification and regression tasks, as measured by F1 score and MSE. Under limited budgets, DMCO achieves up to 82.19% of the performance of full data cleaning with exhaustive AutoML, while remaining robust across different AutoML frameworks.

Machine learning systems often rely on two separate steps: first improving the data, and then searching for a good model. In practice, these two steps affect each other, especially when time, computing resources, or data-cleaning budgets are limited. This paper introduces DMCO, a framework that coordinates data cleaning and automated model selection within a fixed budget. Instead of cleaning all data first and then running model search, DMCO alternates between cleaning the most useful data samples and improving the model. Experiments on real-world datasets show that this coordinated process can achieve better model performance than treating data cleaning and model optimization as separate stages, while using resources more efficiently.