2015/05/26 by Andreas Grünauer, Grünauer, Andreas, Markus Vincze +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · Medicine · Physics and Astronomy · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Machine Learning in Bioinformatics #Machine Learning in Materials Science #Metabolomics and Mass Spectrometry Studies #Nuclear Physics and Applications #Radiomics and Machine Learning in Medical Imaging #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.1505.06907
Presented at OAGM Workshop, 2015 (arXiv:1505.01065)
arxiv created 2015/05/26 · openalex publication_date 2015/05/26 · arxiv updated 2015/05/27 · openalex created_date 2022/08/30 · openalex updated_date 2026/07/28
In this work we show that the classification performance of high-dimensional structural MRI data with only a small set of training examples is improved by the usage of dimension reduction methods. We assessed two different dimension reduction variants: feature selection by ANOVA F-test and feature transformation by PCA. On the reduced datasets, we applied common learning algorithms using 5-fold cross-validation. Training, tuning of the hyperparameters, as well as the performance evaluation of the classifiers was conducted using two different performance measures: Accuracy, and Receiver Operating Characteristic curve (AUC). Our hypothesis is supported by experimental results.