Perusquia, José A;
Griffin, Jim E;
Villa, Cristiano;
(2025)
Beta-CoRM: A Bayesian Approach for n-gram Profiles Analysis.
Computational Statistics and Data Analysis
, 202
, Article 108056. 10.1016/j.csda.2024.108056.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Beta-CoRM: A Bayesian Approach for n-gram Profiles Analysis |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.csda.2024.108056 |
| Publisher version: | https://doi.org/10.1016/j.csda.2024.108056 |
| Language: | English |
| Additional information: | © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Bayesian statistics, feature selection, labeled data ,n-grams, supervised learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10196456 |
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