# Area-norm COBRA on Conditional Survival Prediction

arXiv: https://arxiv.org/abs/2309.00417 · HTML: https://mlsanity.com/paper/2309.00417/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 1 Sep 2023.

## Builds on (most cited first, 10 of 10)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Effective Ways to Build and Evaluate Individual Survival Distributions](https://mlsanity.com/paper/1811.11347/) (2018)
- [SurvLIME: A method for explaining machine learning survival models](https://mlsanity.com/paper/2003.08371/) (2020)
- [SurvSHAP(t): Time-dependent explanations of machine learning survival models](https://mlsanity.com/paper/2208.11080/) (2022)
- [Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates](https://mlsanity.com/paper/1905.05285/) (2019)
- [A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds](https://mlsanity.com/paper/2005.02249/) (2020)
- [SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models](https://mlsanity.com/paper/2005.02387/) (2020)
- [Concordance based Survival Cobra with regression type weak learners](https://mlsanity.com/paper/2209.11919/) (2022)
- [Integrated Brier Score based Survival Cobra -- A regression based approach](https://mlsanity.com/paper/2210.12006/) (2022)

## Built on by (most cited first, 1 of 1)

- [KESurv: A Kernel Ensemble Method for Patient-Specific Survival Prediction](https://mlsanity.com/paper/2610.06434/) (2026)

## Abstract (arXiv)

The paper explores a different variation of combined regression strategy to calculate the conditional survival function. We use regression based weak learners to create the proposed ensemble technique. The proposed combined regression strategy uses proximity measure as area between two survival curves. The proposed model shows a construction which ensures that it performs better than the Random Survival Forest. The paper discusses a novel technique to select the most important variable in the combined regression setup. We perform a simulation study to show that our proposition for finding relevance of the variables works quite well. We also use three real-life datasets to illustrate the model.