Supervised classification of categorical data with uncertain labels for DNA barcoding

In the supervised classification framework, the human supervision is required for labeling a set of learning data which are then used for building the classifier. However, in many applications, the human supervision is either imprecise, difficult or expensive and this gives rise to non robust classifiers. An interesting application where this situation occurs is DNA barcoding which aims to develop a standard tool to identify species with no or limited recourse to taxonomic expertise. In some cases, the morphological features describing the reference sample may be misleading and the taxonomists attribute labels incorrectly. This work presents a robust supervised classification method for categorical data based on a multivariate multinomial mixture model. The proposed method is applied to DNA barcoding and compared to classical methods on a real dataset.