2007/04/25 by Tshilidzi Marwala, Marwala, Tshilidzi, Bodie Crossingham +1
Computer Science · #Artificial Intelligence (cs.AI) #Data Management and Algorithms #Data Mining Algorithms and Applications #FOS: Computer and information sciences #I.2.6 #Rough Sets and Fuzzy Logic #cs.AI
paper · pdf · doi:10.48550/arxiv.0704.3433
20 pages, 3 figures
arxiv created 2007/04/25 · openalex publication_date 2007/04/25 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance criteria. The proposed method is tested to estimate the risk of HIV given demographic data. The results obtained shows that the proposed approach is able to achieve an average accuracy of 58% with the accuracy varying up to 66%. In addition the Bayesian rough set give the probabilities of the estimated HIV status as well as the linguistic rules describing how the demographic parameters drive the risk of HIV.