(i) Need to combine CFS Subset Evaluator with Bayesian Network learning algorithm
(ii) CFS comes under attribute selection. So the features selected by CFS should be used in Bayesian Learning.
(iiii) Each feature in the list of selected features should be added in an incremental manner (one by one) from the first till all of the features are added and the result of Bayesian learning should be recorded at each step. -Incremental Feature Selection
Summarizing, the combined algorithm should come under classification algorithms. First CFS should be run with default parameters . The result of cfs should be used in the Bayesian algorithm. It should appear as a single algorithm. Then Incremental Feature selection should take place where each feature from the list of selected features should be used for classification and the performance parameters (MCC, Accuracy, Sensitivity and SPecificity) should be recorded for each step.
Please use Weka 3.7.7- All parameters are calculated in that
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My bachelor faculty is Computer scieince, and my master degree is about Electronic Engineering, I am very familar with these kinds of algorithm, I can do this for you.