Search: onr:"swepub:oai:DiVA.org:hh-52468" >
Improving Concordan...
Improving Concordance Index in Regression-based Survival Analysis : Discovery of Loss Function for Neural Networks
-
- Altarabichi, Mohammed Ghaith, 1981- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
-
- Alabdallah, Abdallah, 1979- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
-
- Pashami, Sepideh, 1985- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
-
show more...
-
- Ohlsson, Mattias, 1967- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
-
- Rögnvaldsson, Thorsteinn, 1963- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
-
- Nowaczyk, Sławomir, 1978- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
-
show less...
-
(creator_code:org_t)
- 2024
- English.
- Related links:
-
https://urn.kb.se/re...
Abstract
Subject headings
Close
- In this work, we use an Evolutionary Algorithm (EA) to discover a novel Neural Network (NN) regression-based survival loss function with the aim of improving the C-index performance. Our contribution is threefold; firstly, we propose an evolutionary meta-learning algorithm SAGA$_{loss}$ for optimizing a neural-network regression-based loss function that maximizes the C-index; our algorithm consistently discovers specialized loss functions that outperform MSCE. Secondly, based on our analysis of the evolutionary search results, we highlight a non-intuitive insight that signifies the importance of the non-zero gradient for the censored cases part of the loss function, a property that is shown to be useful in improving concordance. Finally, based on this insight, we propose MSCE$_{Sp}$, a novel survival regression loss function that can be used off-the-shelf and generally performs better than the Mean Squared Error for censored cases. We performed extensive experiments on 19 benchmark datasets to validate our findings.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Keyword
- evolutionary meta-learning
- loss function
- neural networks
- survival analysis
- regression
Publication and Content Type
- vet (subject category)
- ovr (subject category)
To the university's database