Generating Graphical Chain by Mutual Matching of Bayesian Network and Extracted Rules of Bayesian Network Using Genetic Algorithm
This work addresses the need for interpretable decision support in data analysis, though it is incremental as it builds on existing rule extraction and genetic algorithm techniques.
The paper tackles the problem of making Bayesian networks more understandable for decision-making by extracting rules using a genetic algorithm and generating a graphical chain through mutual matching, achieving comparable results to brute force methods with significantly lower computational cost on small networks.
With the technology development, the need of analyze and extraction of useful information is increasing. Bayesian networks contain knowledge from data and experts that could be used for decision making processes But they are not easily understandable thus the rule extraction methods have been used but they have high computation costs. To overcome this problem we extract rules from Bayesian network using genetic algorithm. Then we generate the graphical chain by mutually matching the extracted rules and Bayesian network. This graphical chain could shows the sequence of events that lead to the target which could help the decision making process. The experimental results on small networks show that the proposed method has comparable results with brute force method which has a significantly higher computation cost.