Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Diversity versus Quality in Classification Ensembles Based on Feature Selection
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Ensemble methods for classification of patients for personalized medicine with high-dimensional data
Artificial Intelligence in Medicine
Data Mining on Imbalanced Data Sets
ICACTE '08 Proceedings of the 2008 International Conference on Advanced Computer Theory and Engineering
ACM SIGKDD Explorations Newsletter
Texture analysis in quantitative osteoporosis assessment: characterizing microarchitecture
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
3D Image Analysis and Artificial Intelligence for Bone Disease Classification
Journal of Medical Systems
A classifier ensemble approach for the missing feature problem
Artificial Intelligence in Medicine
Explaining the output of ensembles in medical decision support on a case by case basis
Artificial Intelligence in Medicine
Ensemble canonical correlation analysis
Applied Intelligence
Hi-index | 12.05 |
Areal bone mineral density (aBMD) is used in clinical practice to diagnose osteoporosis. In previous studies, aBMD was estimated from diagnostic computed tomography (dCT) images, but a battery of medical tests was also taken that can be used to improve the regression performance. However, it is difficult to exploit the multimodal data as the additional features have poor informativeness and may lead to overfitting. An ensemble-based framework is proposed to improve the regression accuracy and robustness on multimodal medical data with a high relative dimensionality. Instead of case-wise bootstrap aggregating, a filtering-based metalearner scheme was employed to build feature-wise ensembles. The proposed approach was evaluated on clinical data and was found to be superior to bagging and other ensemble methods. The feature-wise ensembling approach can also be used to automatically determine if any multimodal features are related to bone mineral density. Several blood measurements were identified to be linked with bone mineral density, and a literature search supported the automatic identification results.