  In this paper, a new feature selection algorithm, called SFE (Simple, Fast,
and Efficient), is proposed for high-dimensional datasets. The SFE algorithm
performs its search process using a search agent and two operators:
non-selection and selection. It comprises two phases: exploration and
exploitation. In the exploration phase, the non-selection operator performs a
global search in the entire problem search space for the irrelevant, redundant,
trivial, and noisy features, and changes the status of the features from
selected mode to non-selected mode. In the exploitation phase, the selection
operator searches the problem search space for the features with a high impact
on the classification results, and changes the status of the features from
non-selected mode to selected mode. The proposed SFE is successful in feature
selection from high-dimensional datasets. However, after reducing the
dimensionality of a dataset, its performance cannot be increased significantly.
In these situations, an evolutionary computational method could be used to find
a more efficient subset of features in the new and reduced search space. To
overcome this issue, this paper proposes a hybrid algorithm, SFE-PSO (particle
swarm optimization) to find an optimal feature subset. The efficiency and
effectiveness of the SFE and the SFE-PSO for feature selection are compared on
40 high-dimensional datasets. Their performances were compared with six
recently proposed feature selection algorithms. The results obtained indicate
that the two proposed algorithms significantly outperform the other algorithms,
and can be used as efficient and effective algorithms in selecting features
from high-dimensional datasets.
