Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...

This paper is framed in the field of statistical face analysis. In particular, the problem of accurate segmentation of prominent features of the face in frontal view images is addressed. Our method constitutes an extension of Cootes et al. [6] linear Active Shape Model (ASM) approach, which has already been used in this task [9]. The technique is built upon the development of a non-linear appea...

This paper discusses a practical framework for the semi{ automatic construction of evaluation functions for games. Based on a structured evaluation function representation, a procedure for exploring the feature space is presented that is able to discover new features in a computational feasible way. Besides the theoretical aspects, related practical issues such as the generation of training pos...

In Streaming Feature Selection (SFS), new features are sequentially considered for addition to a predictive model. When the space of potential features is large, SFS offers many advantages over methods in which all features are assumed to be known in advance. Features can be generated dynamically, focusing the search for new features on promising subspaces, and overfitting can be controlled by ...

Multi-scale Local Binary Pattern based operators are used to extract features from duodenal texture patches with histological ground truth in case of pediatric celiac disease. The multi-scale LBP combined with color channels and possibly other filters lead to a high number of computed histograms. The impact of histogram subset selection on the overall classification rates using two feature subs...