Research on the integrated system of case-based reasoning and Bayesian network

Abstract

With the arrival of big data era, the integration of Case-Based Reasoning (CBR) and Bayesian Network (BN) has become increasingly a promising technology in implementing the intelligence of engineering application. To further improve the efficiency of the integrated system and make it adapt to the large number of parameter under big data, Within-Cross (WC) algorithm is proposed in this paper to assigned big data to each slave node of Hadoop platform for parallel data processing. The WC algorithm can greatly reduce the time costing of parallel data processing and thus improve the efficiency of the integrated system by fully using the computation resource of Hadoop platform. To further enhance the accuracy of the integrated system, this paper proposes a new method called Weighted Super Parameters of Dirichlet Distribution (WSPDD) algorithm to perform probability learning. The WSPDD algorithm gives a weight to each super parameter of Dirichlet Distribution of all problem parameters to adjust the result of probability statistics and then improve probability learning. Therefore, the accuracy of the integrated system is greatly improved. Finally, an application domain is taken as a case study to validate the proposed method.