CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning

• URL: http://arxiv.org/abs/2502.03946v1
• Date: Thu, 06 Feb 2025 10:33:37 GMT
• Title: CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning
• Authors: Yousef Koka, David Selby, Gerrit Großmann, Sebastian Vollmer,
• Abstract summary: Data preprocessing is a critical yet frequently neglected aspect of machine learning. CleanSurvival is a reinforcement-learning-based solution for optimizing preprocessing pipelines. It can handle continuous and categorical variables, using Q-learning to select which combination of data imputation, outlier detection and feature extraction techniques achieves optimal performance.
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• Abstract: Data preprocessing is a critical yet frequently neglected aspect of machine learning, often paid little attention despite its potentially significant impact on model performance. While automated machine learning pipelines are starting to recognize and integrate data preprocessing into their solutions for classification and regression tasks, this integration is lacking for more specialized tasks like survival or time-to-event models. As a result, survival analysis not only faces the general challenges of data preprocessing but also suffers from the lack of tailored, automated solutions in this area. To address this gap, this paper presents 'CleanSurvival', a reinforcement-learning-based solution for optimizing preprocessing pipelines, extended specifically for survival analysis. The framework can handle continuous and categorical variables, using Q-learning to select which combination of data imputation, outlier detection and feature extraction techniques achieves optimal performance for a Cox, random forest, neural network or user-supplied time-to-event model. The package is available on GitHub: https://github.com/datasciapps/CleanSurvival Experimental benchmarks on real-world datasets show that the Q-learning-based data preprocessing results in superior predictive performance to standard approaches, finding such a model up to 10 times faster than undirected random grid search. Furthermore, a simulation study demonstrates the effectiveness in different types and levels of missingness and noise in the data.

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