Abstract: In the process of treating uncertainties of fuzziness and randomness in real regression application, fuzzy random regression was introduced to address the limitation of classical regression which can only fit precise data. However, there is no systematic procedure to identify randomness by means of probability theories. Besides, the existing model mostly concerned in fuzzy equation without considering the discussion on probability equation though random plays a pivotal role in fuzzy random regression model. Hence, this paper proposed a systematic procedure of Naïve Bayes to estimate the probabilities value to overcome randomness. From the result, it shows that the accuracy of Naïve Bayes model can be improved by considering the probability estimation.

Hamijah Mohd Rahman, Nureize Arbaiy, Chuah Chai Wen and Pei-Chun Lin. “Estimating Probability Values Based on Naïve Bayes for Fuzzy Random Regression Model”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.8 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140863

@article{Rahman2023,
title = {Estimating Probability Values Based on Naïve Bayes for Fuzzy Random Regression Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140863},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140863},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Hamijah Mohd Rahman and Nureize Arbaiy and Chuah Chai Wen and Pei-Chun Lin}
}