## 2 Background
### Feature Selection Approaches Based on Particle Swarm Optimization  
The authors of [24] apply PSO in feature selection, considering three goals, which are also targeted by the approach presented in this research article: (1) the maximization of the classification performance, (2) the minimization of the number of the selected features and (3) the minimization of the computational time. However, they propose, as future research work, the development of an approach based on PSO, which is multi-objective and which simultaneously minimizes the number of the selected features and maximizes the performance of the classification. In the approach presented in this article, both of those two objectives are considered in the definition of the objective function. In [25], a method is presented in which the features are selected using a hybrid version of PSO, which also integrates a local search strategy. That method is called hybrid particle swarm optimization with local search (HPSO-LS) and determines efficiently the discriminative features with reduced correlations. The objective function of that approach uses the k-nearest neighbor (k-NN) [26] method, and, between two solutions that return the same accuracy, it considers that solution with the smaller number of features. However, that approach is expensive in terms of computational resources, as it requires the training of a k-NN classifier for each particle in each iteration of the algorithm. The authors of [27] considered an algorithm for feature selection based on PSO with learning memory (PSO-LM). Compared to the approach from this article, that approach introduces a memory learning strategy with the objective to balance the global exploration and the local exploitation in the algorithm. Moreover, their objective function is similar to the objective function of the adapted version of the BPSO algorithm described in this article, as it uses two parts as follows: The first part corresponds to the accuracy of the prediction model trained using the selected features and the second part corresponds to the ratio between the number of the selected features and the total number of features. However, the approach presented in this article is different, as, instead of using a classifier for the first part of the objective function as in the case of that approach, which considers the k-NN classifier, this article introduces an algorithm, which considers the variability of the monitored data, and which also considers the fact that the data is imbalanced. In [28], an approach is considered for the joint moment prediction, in which the features are selected using a method based on BPSO and the objective function is based on the variance accounted for (VAF). Similar to the approach from this article, their approach is tested and validated on DLAs data. However, that approach does not consider the minimization of the features as a distinctive part of the objective function and the data considered in their experiments for the validation of the approach contains only gait patterns of running, therefore it can not be generalized very easily. Another approach for feature selection based on PSO is presented in [29]. The method presented in that article is called hybrid particle swarm optimization with a spiral-shaped mechanism (HPSO-SSM) and is based on the following three improvements: (1) the enhancement of the diversity in the searching process using a logistic map sequence, (2) the introduction of two new parameters in the original formulas, used in the updating of the positions, and (3) the adopting of a spiral-shaped mechanism as a local search operator. Their method presents broad applications, as it is validated using twenty classic benchmark classification datasets from the University of California, Irvine (UCI) machine learning repository. However, compared to the method presented in this article, it is not very focused, as it does not exploit the nature of those datasets at maximum.