Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Describes a data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus,  propose a hybrid algorithm of the two approaches, which consists of two phases, namely, the conditional independence test and the search phases. A new operator is introduced to further enhance the search efficiency.  conduct a number of experiments and compare the hybrid algorithm with our previous algorithm, MDLEP, which uses EP for network learning. The empirical results illustrate that the new approach has better performance.  apply the approach to data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with the models generated by other methods. In the comparison, the induced Bayesian networks produced by the new algorithm outperform the other models.
['Man Leung Wong', 'Shing Yan Lee', 'Kwong Sak Leung']
I am writing a research paper on a idea A hybrid approach to discover Bayesian networks from databases using evolutionary programming
[{"from": "human", "value": "I am writing a research paper on a idea A hybrid approach to discover Bayesian networks from databases using evolutionary programming"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Describes a data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus,  propose a hybrid algorithm of the two approaches, which consists of two phases, namely, the conditional independence test and the search phases. A new operator is introduced to further enhance the search efficiency.  conduct a number of experiments and compare the hybrid algorithm with our previous algorithm, MDLEP, which uses EP for network learning. The empirical results illustrate that the new approach has better performance.  apply the approach to data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with the models generated by other methods. In the comparison, the induced Bayesian networks produced by the new algorithm outperform the other models."}]