Using Dimension Reduction to Improve the Classification of High-dimensional Data
This work addresses the challenge of handling high-dimensional data in medical imaging for researchers and practitioners, but it is incremental as it applies existing dimension reduction techniques to a specific dataset.
The study tackled the problem of classifying high-dimensional structural MRI data with limited training examples by applying dimension reduction methods, resulting in improved classification performance as measured by accuracy and AUC through 5-fold cross-validation.
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.