Selection of artificial neural network models for survival analysis with genetic algorithms

DOI10.1016/J.CSDA.2007.05.001zbMath1452.62792OpenAlexW1991034947WikidataQ61851724 ScholiaQ61851724MaRDI QIDQ1020781

Elia Biganzoli, Nicola Lama, Federico Ambrogi, Patrizia Boracchi

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.05.001

zbMATH Keywords

regularizationneural networksgenetic algorithmscompeting risks

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Statistical aspects of information-theoretic topics (62B10)

Related Items (4)

Nonlinear survival regression using artificial neural network ⋮ Partial logistic relevance vector machines in survival analysis ⋮ Editorial: 2nd special issue on applications of optimization heuristics to estimation and modelling problems ⋮ A new genetic algorithm in proteomics: feature selection for SELDI-TOF data

Uses Software

• R
• MASS (R)
• Hmisc
• BRENT

Cites Work

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• Statistical Analysis of Financial Data in S-Plus
• Logistic Regression, Survival Analysis, and the Kaplan-Meier Curve
• The Predictive Sample Reuse Method with Applications
• An information approach to regularization parameter selection under model misspecification
• A Simplex Method for Function Minimization

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