Generalizing Conditional Naive Bayes Model

Sahar Yazdi, Fatma Najar, Nizar Bouguila

2024

Abstract

Given the fact that the prevalence of big data continues to evolve, the importance of information retrieval techniques becomes increasingly crucial. Numerous models have been developed to uncover the latent structure within data, aiming to extract necessary information or categorize related patterns. However, data is not uniformly distributed, and a substantial portion often contains empty or missing values, leading to the challenge of ”data sparsity”. Traditional probabilistic models, while effective in revealing latent structures, lack mechanisms to address data sparsity. To overcome this challenge, we explored generalized forms of the Dirichlet distributions as priors to hierarchical Bayesian models namely the generalized Dirichlet distribution (LGD-CNB model) and the Beta-Liouville distribution (LBL-CNB model). Our study evaluates the performance of these models in two sets of experiments, employing Gaussian and Discrete distributions as examples of exponential family distributions. Results demonstrate that using GD distribution and BL distribution as priors enhances the model learning process and surpass the performance of the LD-CNB model in each case.

Paper Citation

in Harvard Style

Yazdi S., Najar F. and Bouguila N. (2024). Generalizing Conditional Naive Bayes Model. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 163-171. DOI: 10.5220/0012524000003690

in Bibtex Style

@conference{iceis24,
author={Sahar Yazdi and Fatma Najar and Nizar Bouguila},
title={Generalizing Conditional Naive Bayes Model},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={163-171},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012524000003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Generalizing Conditional Naive Bayes Model
SN - 978-989-758-692-7
AU - Yazdi S.
AU - Najar F.
AU - Bouguila N.
PY - 2024
SP - 163
EP - 171
DO - 10.5220/0012524000003690
PB - SciTePress