Abstract: Feature selection is an important part of building a machine learning model.
By eliminating redundant or misleading features from data, the machine learning
model can achieve better performance while reducing the demand on com-puting
resources. Metaheuristic algorithms are mostly used to implement feature
selection such as swarm intelligence algorithms and evolutionary algorithms.
However, they suffer from the disadvantage of relative complexity and slowness.
In this paper, a concise method is proposed for universal feature selection.
The proposed method uses a fusion of the filter method and the wrapper method,
rather than a combination of them. In the method, one-hoting encoding is used
to preprocess the dataset, and random forest is utilized as the classifier. The
proposed method uses normalized frequencies to assign a value to each feature,
which will be used to find the optimal feature subset. Furthermore, we propose
a novel approach to exploit the outputs of mutual information, which allows for
a better starting point for the experiments. Two real-world dataset in the
field of intrusion detection were used to evaluate the proposed method. The
evaluation results show that the proposed method outperformed several
state-of-the-art related works in terms of accuracy, precision, recall, F-score