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