## 2 Background
### Feature Selection Challenges for Imbalanced Data Generated by Monitoring Sensors  
In [18] the high dimensionality and the class imbalance properties of the datasets are tackled. Compared to the approach presented in this article, the authors of that article are motivated by medical applications from real-world systems. Their feature selection method aims to achieve a better separability between the minority class and the majority class. In the approach presented in this article the majority or the minority properties of the classes of the samples are not considered when the features are selected. However, the approach presented in this article considers the variability of the monitored data for each class for each feature, and it also considers the relations between features.  
The authors of [19] address one of the major challenges of the feature selection in the case of datasets with many features, namely the ignorance of the class imbalance problem and thus in the majority of the cases, the selected features are biased towards majority classes. In the approach presented in this article the imbalance is addressed by the way in which the objective function of the adapted version of the BPSO algorithm is defined, as all the classes are treated equally.  
In [20], the authors deal with the following two major challenges in machine learning, namely the high dimensionality and the class-imbalance. The cardinality of the set of features is penalized by a scaling factors technique, while the class-imbalance problem is addressed using a method based on a support vector machine (SVM) [21]. In the approach presented in this article, each class is treated equally when the objective function is defined, and moreover, the number of features is penalized in the objective function, increasing its value when the number of features is small.  
The selection of features for imbalanced datasets is challenging as an effective learning model should be constructed, reducing the memory consumption and the time consumption--two objectives, which are addressed in the approach presented in this article. In [22] that challenge is addressed by rough-set-based feature selection algorithm for imbalanced data (RSFSAID), a novel algorithm for feature selection. Similar to the approach from this article, those authors use PSO. However, in their approach, PSO is applied in the determination of the optimized parameters for the proposed algorithm. They also study the lower and the upper boundary regions, when they define the significance of the features. In the approach from this article the minimum and the maximum values of the ranges of variability are considered in the definition of the matrix of variability.  
The authors of [23] address the challenge of feature selection in the case of high dimensional imbalanced datasets from the perspective of the neglecting of the features interaction. Many traditional approaches applied in the case of datasets with imbalanced classes are biased towards the majority classes and the authors of that article propose a method for feature selection based on the interaction information (II). In this article the interactions between the features are considered, defining a matrix that approaches the features from the perspective of the range of variability. Moreover, this article considers particular characteristics of the DLAs datasets that are not approached in that article, and proposes a more generic algorithm for feature selection.