Concordance based Survival Cobra with regression type weak learners
- URL: http://arxiv.org/abs/2209.11919v1
- Date: Sat, 24 Sep 2022 04:10:17 GMT
- Title: Concordance based Survival Cobra with regression type weak learners
- Authors: Rahul Goswami, Arabin Kumar Dey
- Abstract summary: We take weak learners as different random survival trees. We propose to maximize concordance in the right-censored set up to find the optimal parameters.
Our proposed formulations use two different norms, say, Max-norm and Frobenius norm, to find a proximity set of predictions from query points in the test dataset.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In this paper, we predict conditional survival functions through a combined
regression strategy. We take weak learners as different random survival trees.
We propose to maximize concordance in the right-censored set up to find the
optimal parameters. We explore two approaches, a usual survival cobra and a
novel weighted predictor based on the concordance index. Our proposed
formulations use two different norms, say, Max-norm and Frobenius norm, to find
a proximity set of predictions from query points in the test dataset. We
illustrate our algorithms through three different real-life dataset
implementations.
Related papers
- Variational Deep Survival Machines: Survival Regression with Censored Outcomes [11.82370259688716]
Survival regression aims to predict the time when an event of interest will take place, typically a death or a failure.
We present a novel method to predict the survival time by better clustering the survival data and combine primitive distributions.
arXiv Detail & Related papers (2024-04-24T02:16:00Z) - Regression Trees for Fast and Adaptive Prediction Intervals [2.6763498831034043]
We present a family of methods to calibrate prediction intervals for regression problems with local coverage guarantees.
We create a partition by training regression trees and Random Forests on conformity scores.
Our proposal is versatile, as it applies to various conformity scores and prediction settings.
arXiv Detail & Related papers (2024-02-12T01:17:09Z) - Area-norm COBRA on Conditional Survival Prediction [0.0]
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 model shows a construction which ensures that it performs better than the Random Survival Forest.
arXiv Detail & Related papers (2023-09-01T12:20:30Z) - Integrated Brier Score based Survival Cobra -- A regression based
approach [0.0]
We provide two novel regression-based integrations of combined regression strategy (COBRA) ensemble using Integrated Brier Score to predict conditional survival function.
Our proposition includes a weighted version of all predictions based on Integrated Brier Score score made by all weak learners to predict the final survival function apart from the straight implementation.
arXiv Detail & Related papers (2022-10-21T14:48:10Z) - Fair Wrapping for Black-box Predictions [105.10203274098862]
We learn a wrapper function which we define as an alpha-tree, which modifies the prediction.
We show that our modification has appealing properties in terms of composition ofalpha-trees, generalization, interpretability, and KL divergence between modified and original predictions.
arXiv Detail & Related papers (2022-01-31T01:02:39Z) - Are Missing Links Predictable? An Inferential Benchmark for Knowledge
Graph Completion [79.07695173192472]
InferWiki improves upon existing benchmarks in inferential ability, assumptions, and patterns.
Each testing sample is predictable with supportive data in the training set.
In experiments, we curate two settings of InferWiki varying in sizes and structures, and apply the construction process on CoDEx as comparative datasets.
arXiv Detail & Related papers (2021-08-03T09:51:15Z) - Near-optimal inference in adaptive linear regression [60.08422051718195]
Even simple methods like least squares can exhibit non-normal behavior when data is collected in an adaptive manner.
We propose a family of online debiasing estimators to correct these distributional anomalies in at least squares estimation.
We demonstrate the usefulness of our theory via applications to multi-armed bandit, autoregressive time series estimation, and active learning with exploration.
arXiv Detail & Related papers (2021-07-05T21:05:11Z) - Multivariate Probabilistic Regression with Natural Gradient Boosting [63.58097881421937]
We propose a Natural Gradient Boosting (NGBoost) approach based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution.
Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches.
arXiv Detail & Related papers (2021-06-07T17:44:49Z) - Double Perturbation: On the Robustness of Robustness and Counterfactual
Bias Evaluation [109.06060143938052]
We propose a "double perturbation" framework to uncover model weaknesses beyond the test dataset.
We apply this framework to study two perturbation-based approaches that are used to analyze models' robustness and counterfactual bias in English.
arXiv Detail & Related papers (2021-04-12T06:57:36Z) - Set Prediction without Imposing Structure as Conditional Density
Estimation [40.86881969839325]
We propose an alternative to training via set losses by viewing learning as conditional density estimation.
Our framework fits deep energy-based models and approximates the intractable likelihood with gradient-guided sampling.
Our approach is competitive with previous set prediction models on standard benchmarks.
arXiv Detail & Related papers (2020-10-08T16:49:16Z) - Adaptive Correlated Monte Carlo for Contextual Categorical Sequence
Generation [77.7420231319632]
We adapt contextual generation of categorical sequences to a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control.
We also demonstrate the use of correlated MC rollouts for binary-tree softmax models, which reduce the high generation cost in large vocabulary scenarios.
arXiv Detail & Related papers (2019-12-31T03:01:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.