Drousiotis, Efthyvoulos 
ORCID: 0000-0002-9746-456X, Phillips, Alexander M, Spirakis, Paul G ORCID: 0000-0001-5396-3749 and Maskell, Simon ORCID: 0000-0003-1917-2913
(2023)
Bayesian Decision Trees Inspired from Evolutionary Algorithms.
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Abstract
Bayesian Decision Trees (DTs) are generally considered a more advanced and accurate model than a regular Decision Tree (DT) as they can handle complex and uncertain data. Existing work on Bayesian DTs uses Markov Chain Monte Carlo (MCMC) with an accept-reject mechanism and sample using naive proposals to proceed to the next iteration. This method can be slow because of the burn-in time needed. We can reduce the burn-in period by proposing a more sophisticated way of sampling or by designing a different numerical Bayesian approach. In this paper, we propose a replacement of the MCMC with an inherently parallel algorithm, the Sequential Monte Carlo (SMC), and a more effective sampling strategy inspired by the Evolutionary Algorithms (EA). Experiments show that SMC combined with the EA can produce more accurate results compared to MCMC in 100 times fewer iterations.
| Item Type: | Conference or Workshop Item (Unspecified) |
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| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 21 Nov 2023 13:56 |
| Last Modified: | 22 Nov 2023 09:13 |
| DOI: | 10.1007/978-3-031-44505-7_22 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3176933 |
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