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The role of hyper-parameters in relational topic models: Prediction capabilities vs topic quality.
- Source :
-
Information Sciences . Jun2023, Vol. 632, p252-268. 17p. - Publication Year :
- 2023
-
Abstract
- In this paper, we investigate the impact of optimal hyper-parameter configuration in relational topic models. The main goal is to validate the hypothesis that single-objective Bayesian Optimization (BO) can discover a hyper-parameter setting that leads a set of relational topic models to simultaneously ensure good prediction capabilities and significant topics from a qualitative perspective. Our research, as a result of a comparative analysis performed on 7 state-of-the-art models, 5 performance measures and 3 datasets, has highlighted three main findings: (1) the majority of relational topic models are not able to offer a good trade-off between classification capabilities and topic interpretability; (2) single-objective optimization of hyper-parameters, targeted on maximizing the F1-Measure, is able to create topics that are also optimal with respect to the Kullback Leibler divergence measure; (3) the Pareto frontiers across several performance metrics reveals that the most promising trade-off between the performance metrics can be obtained by Constrained Relational Topic Models. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PREDICTION models
*ALTMETRICS
*MUSIC orchestration
*COMPARATIVE studies
Subjects
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 632
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 162758373
- Full Text :
- https://doi.org/10.1016/j.ins.2023.02.076