An Artificial Intelligent Methodology-based Bayesian Belief Networks Constructing for Big Data Economic Indicators Prediction

Economic indicator prediction in big data requires treating all random variables as an independent set of selective values and used as a discriminative method for classification tasks. A Bayesian network is a popular graphical representation approach for modeling probabilistic dependencies and causality among a set of random variables to incorporate a huge amount of human expert knowledge about the problem of interest involving diagnostic reasoning of big data. In our study, we set out to construct the Bayesian networks using the standard error for a least-squares linear regression (STE) and the domain knowledge from the literature in the field for predicting the big data economy prediction. The experimental results show that the proposed STE baseline provided us with an accuracy of 20% to 58% in seven out of eight regions, including the aggregate for “World”. In comparison, the Bayesian Networks generated by our first Domain Knowledge Model improved accuracy from 54% to 75% in the same regions.

@article{Al-Azzawi2023,
  title     = {An Artificial Intelligent Methodology-based Bayesian Belief Networks Constructing for Big Data Economic Indicators Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {5},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Adil Al-Azzawi and Fernando Torre Mora and Chanmann Lim and Yi Shang},
  doi       = {10.14569/IJACSA.2023.0140588},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140588}
}