Abstract
This paper presents a fuzzy relational approach to discrete-time survival analysis based on fuzzy relational inequalities for modeling and predicting hazard probabilities over time. The proposed method combines data fuzzification with interval-specific fuzzy relational models, providing both accurate prediction and an interpretable description of the relationships between predictors and the risk of failure. Three fuzzification methods (sigmoid, Gaussian, and Gaussian smoothing) and four t-norms (minimum, product, Lukasiewicz, and Fodor) were investigated. The proposed approach was evaluated on three benchmark datasets. The experimental results demonstrate that both the fuzzification method and the choice of t-norm affect prediction performance, with Gaussian smoothing and the Lukasiewicz t-norm providing the best overall results. The proposed framework offers a flexible and interpretable tool for discrete-time survival analysis, particularly for datasets characterized by uncertainty, gradual transitions, or nonlinear relationships.
Paper Type
Poster
DOI
10.62036/ISD.2026.79
Modeling and prediction methods in discrete-time survival analysis using fuzzy techniques
This paper presents a fuzzy relational approach to discrete-time survival analysis based on fuzzy relational inequalities for modeling and predicting hazard probabilities over time. The proposed method combines data fuzzification with interval-specific fuzzy relational models, providing both accurate prediction and an interpretable description of the relationships between predictors and the risk of failure. Three fuzzification methods (sigmoid, Gaussian, and Gaussian smoothing) and four t-norms (minimum, product, Lukasiewicz, and Fodor) were investigated. The proposed approach was evaluated on three benchmark datasets. The experimental results demonstrate that both the fuzzification method and the choice of t-norm affect prediction performance, with Gaussian smoothing and the Lukasiewicz t-norm providing the best overall results. The proposed framework offers a flexible and interpretable tool for discrete-time survival analysis, particularly for datasets characterized by uncertainty, gradual transitions, or nonlinear relationships.
Recommended Citation
Kretowska, M. & Matusiewicz, Z.(2026). Modeling and prediction methods in discrete-time survival analysis using fuzzy techniques. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.79