title: Classification and visualization based on derived image features: application to genetic syndromes.
abstract: Data transformations prior to analysis may be beneficial in classification tasks. In this article we investigate a set of such transformations on 2D graph-data derived from facial images and their effect on classification accuracy in a high-dimensional setting. These transformations are low-variance in the sense that each involves only a fixed small number of input features. We show that classification accuracy can be improved when penalized regression techniques are employed, as compared to a principal component analysis (PCA) pre-processing step. In our data example classification accuracy improves from 47% to 62% when switching from PCA to penalized regression. A second goal is to visualize the resulting classifiers. We develop importance plots highlighting the influence of coordinates in the original 2D space. Features used for classification are mapped to coordinates in the original images and combined into an importance measure for each pixel. These plots assist in assessing plausibility of classifiers, interpretation of classifiers, and determination of the relative importance of different features.
authors.affiliation: Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Brunilda
authors.initials: B
authors.lastname: Balliu
authors.affiliation: Institut für Neuroinformatik, Ruhr-Universität Bochum, Bochum, Germany.
authors.forename: Rolf P
authors.initials: RP
authors.lastname: Würtz
authors.affiliation: Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Horsthemke
authors.affiliation: Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany.
authors.forename: Dagmar
authors.initials: D
authors.lastname: Wieczorek
authors.affiliation: Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Stefan
authors.initials: S
authors.lastname: Böhringer
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D019465:Craniofacial Abnormalities / Q000145:classification*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*