  The population-based optimization algorithms have provided promising results
in feature selection problems. However, the main challenges are high time
complexity. Moreover, the interaction between features is another big challenge
in FS problems that directly affects the classification performance. In this
paper, an estimation of distribution algorithm is proposed to meet three goals.
Firstly, as an extension of EDA, the proposed method generates only two
individuals in each iteration that compete based on a fitness function and
evolve during the algorithm, based on our proposed update procedure. Secondly,
we provide a guiding technique for determining the number of features for
individuals in each iteration. As a result, the number of selected features of
the final solution will be optimized during the evolution process. The two
mentioned advantages can increase the convergence speed of the algorithm.
Thirdly, as the main contribution of the paper, in addition to considering the
importance of each feature alone, the proposed method can consider the
interaction between features. Thus, it can deal with complementary features and
consequently increase classification performance. To do this, we provide a
conditional probability scheme that considers the joint probability
distribution of selecting two features. The introduced probabilities
successfully detect correlated features. Experimental results on a synthetic
dataset with correlated features prove the performance of our proposed approach
facing these types of features. Furthermore, the results on 13 real-world
datasets obtained from the UCI repository show the superiority of the proposed
method in comparison with some state-of-the-art approaches.
