Tree-Based Integration of One-versus-Some (OVS) Classifiers for Multiclass Classification

Motivated by applications such as gene expression analysis, binary classification has achieved notable success. (e.g., cancer samples versus normal samples) When comes to multiclass classification, the extension is not straightforward. There has been two main directions on such extensions: 1) via a sequence of nested binary classifiers in a classification tree or 2) via classifier ensembles that integrate votes from all one-versus-all (OVA) classifiers or all all-pairs (AP) classifiers. In this article, we present a new way to combine both strategies in a multiclass classification

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a one-vs-one binarization paradigm are enriched by one-vs-all correction classifiers. They are utilized to efficiently classify individual traffic participants and also identify hidden object classes which have not been presented to the classifiers during training. For each classifier of the ensemble an individual feature set is determined from a total set of 98 features. Thereby, the overall classification performance can be improved when compared to previous methods and, additionally, novel classes can be identified much more accurately. Furthermore, the proposed structure allows to give new insights in the importance of features for the recognition of individual classes which is crucial for the development of new algorithms and sensor requirements.Comment: 8 pages, 9 figures, accepted paper for 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, June 201