The Random Forest (RF) algorithm for regression and classification has considerably gained popularity since its introduction in 2001. Meanwhile, it has grown to a standard classification approach competing with logistic regression in many innovation-friendly scientific fields. In this context, we present a large scale benchmarking experiment based on 260 real datasets comparing the prediction p...

We propose a graph cut based automatic method for prostate segmentation using image features, context information and semantic knowledge. A volume of interest (VOI) is first identified using supervoxel oversegmentation and their subsequent classification. All voxels within the VOI are labeled prostate or background using graph cuts. Semantic information obtained from Random forest (RF) classifi...

Coronary Artery Disease (CAD) is one of the most prevalent diseases, which can lead to disability and sometimes even death. Diagnostic procedures of CAD are typically invasive, although they do not satisfy the required accuracy. Hence machine learning methods can be used, so that diagnosis can be made faster and with improved accuracy. There are many features that need to be taken into consider...

Retinal pigment epithelium (RPE) is a principal site of pathogenesis in age-related macular degeneration (AMD). AMD is a main source of vision loss even blindness in the elderly and there is no effective treatment right now. Our aim is to describe the relationship between the morphology of RPE cells and the age and genotype of the eyes. We use principal component analysis (PCA) or functional pr...

For high dimensional data a large portion of features are often not informative of the class of the objects. Random forest algorithms tend to use a simple random sampling of features in building their decision trees and consequently select many subspaces that contain few, if any, informative features. In this paper we propose a stratified sampling method to select the feature subspaces for rand...