Many traditional measures evaluating features individually cannot work well and a subset of features needs to be evaluated as a whole. Therefore, the two key factors in a feature selection approach are the search techniques, which explore the search space to find the optimal feature subset(s), and the evaluation criteria, which measure the quality of feature subsets to guide the search.
the question: is there any other evaluation criteria that used in wrapper method?
Yes Doctor, This research is still the focus of attention of many researchers in recent years. Feature selection is the process of finding the least possible attributes that are able to describe the dataset as the original features do; this in turn improves the performance of the prediction process and provides a better understanding of stored data.
Many metaheuristic algorithms are applied in feature selection problems.
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