A growing body of discoveries in molecular signatures has revealed that volatile organic compounds (VOCs), the small
molecules associated with an individual's odor and breath, can be monitored to reveal the identity and presence of a
unique individual, as well their overall physiological status. Given the analysis requirements for differential VOC
profiling via gas chromatography/mass spectrometry, our group has developed a novel informatics platform, Metabolite
Differentiation and Discovery Lab (MeDDL). In its current version, MeDDL is a comprehensive tool for time-series
spectral registration and alignment, visualization, comparative analysis, and machine learning to facilitate the efficient
analysis of multiple, large-scale biomarker discovery studies. The MeDDL toolset can therefore identify a large
differential subset of registered peaks, where their corresponding intensities can be used as features for classification.
This initial screening of peaks yields results sets that are typically too large for incorporation into a portable, electronic
nose based system in addition to including VOCs that are not amenable to classification; consequently, it is also
important to identify an optimal subset of these peaks to increase classification accuracy and to decrease the cost of the
final system. MeDDL's learning tools include a classifier similar to a K-nearest neighbor classifier used in conjunction
with a genetic algorithm (GA) that simultaneously optimizes the classifier and subset of features. The GA uses ROC
curves to produce classifiers having maximal area under their ROC curve. Experimental results on over a dozen
recognition problems show many examples of classifiers and feature sets that produce perfect ROC curves.
Classification of 3D objects is becoming an increasingly important research area due to cheap and innovative sensor technology. Shadows, noise, viewing direction, and distance from the sensor all directly affect the quality and amount of surface information provided by the sensor. The recognition approach described in this paper converts surface information, a set of (x,y,z) points, into a discrete 3D binary image. This conversion step processes the surface points using a fuzzy technique to mitigate the effects of noise and minor distortions. These images are then processed by sequences of one or two randomly selected morphological operators. Each of the sequences' output is then fed into a simple transducer to obtain a set of scalar feature values. The feature values are classified using a K nearest neighbor (KNN) classifier that is trained using a sparse number of training samples. Experiments were conducted using the Air Force Research Laboratory's E3D data and experimental protocol. Experimental results for the tank classification problem using 10 tanks and 26 confusers are presented. The results show the combination of morphological processing and KNN classifier produced consistently good performance under variations in noise, viewing angle, or distance.