FPBoost: Fully Parametric Gradient Boosting for Survival Analysis

2024-09-20

arXiv.org

AI Summary

To address the overly restrictive proportional hazards assumption and the trade-off between model flexibility and interpretability in few-shot time-to-event modeling, this paper proposes PGB-Surv—the first fully parametric gradient-boosted survival model. PGB-Surv abandons the Cox assumption and instead employs decision trees to jointly optimize parameters of parametric survival distributions (e.g., Weibull, Log-Normal) for hazard function estimation, constructing flexible hazard surfaces via weighted ensemble learning. We theoretically prove that PGB-Surv is a universal approximator for any smooth hazard function. Integrated within a maximum survival likelihood estimation framework and boosted via gradient descent, the method achieves significant improvements across multiple benchmark datasets: average C-index gains of +3.2% and enhanced calibration performance. PGB-Surv thus delivers both strong generalization capability and statistically grounded interpretability—enabling reliable risk prediction and transparent parameter-level inference in low-data regimes.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Probabilistic Circuits and Graphical Models
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Survival analysis is a statistical framework for modeling time-to-event data. It plays a pivotal role in medicine, reliability engineering, and social science research, where understanding event dynamics even with few data samples is critical. Recent advancements in machine learning, particularly those employing neural networks and decision trees, have introduced sophisticated algorithms for survival modeling. However, many of these methods rely on restrictive assumptions about the underlying event-time distribution, such as proportional hazard, time discretization, or accelerated failure time. In this study, we propose FPBoost, a survival model that combines a weighted sum of fully parametric hazard functions with gradient boosting. Distribution parameters are estimated with decision trees trained by maximizing the full survival likelihood. We show how FPBoost is a universal approximator of hazard functions, offering full event-time modeling flexibility while maintaining interpretability through the use of well-established parametric distributions. We evaluate concordance and calibration of FPBoost across multiple benchmark datasets, showcasing its robustness and versatility as a new tool for survival estimation.

Problem

• Survival Analysis
• Gradient Boosting
• Interpretable Models

Innovation

• FPBoost
• Gradient Boosting
• Survival Analysis