Beta-CoRM: A Bayesian approach for n-gram profiles analysis

n -gram profiles have been successfully and widely used to analyse long sequences of potentially differing lengths for clustering or classification. Mainly, machine learning algorithms have been used for this purpose but, despite their predictive performance, these methods cannot discover hidden structures or provide a full probabilistic representation of the data. A novel class of Bayesian generative models designed for n -gram profiles used as binary attributes have been designed to address this. The flexibility of the proposed modelling allows to consider a straightforward approach to feature selection in the generative model. Furthermore, a slice sampling algorithm is derived for a fast inferential procedure, which is applied to synthetic and real data scenarios and shows that feature selection can improve classification accuracy. • We develop a feature allocation model for grouped data with binary attributes and demonstrate its use on n-gram data. • Show how the model can be estimated using a simple, exact Markov chain Monte Carlo method. • Introduce a post-hoc variable selection step which finds variable that maximally discriminate among groups. • The variable selection method leads to better out-of-sample classification accuracy in simulated and real data.