## 2 Basic Concepts
### Feature Selection  
The recent explosion of dataset size, in both number of observations and dimensions, has pushed towards the usage of data reduction techniques. Moreover, increasing dataset size might sometimes produce worse performances in data analytics applications. Furthermore, data reduction can improve storage efficiency and reduce costs. Data reduction can be achieved either by decreasing the number of objects or by decreasing the number of features. Therefore, data reduction is considered a significant preprocessing task for big data analytics techniques [13]. Feature reduction techniques can be divided into two main groups: feature construction and feature selection. Feature construction techniques transform the original features in high-dimensional space into new low-dimensional features. The newly generated features are usually linear or nonlinear functions of the original features, e.g., principal component analysis (PCA) [14], singular value decomposition (SVD) [14], and linear discriminant analysis (LDA) [14]. On the other hand, feature selection aims at removing a group of irrelevant and redundant features, attributes, or variables [15]. As shown in Figure 1, these methods may be classified according to the type of learning algorithms into three main approaches: supervised, unsupervised, and semi-supervised. Unsupervised feature selection techniques do not require labeled data. These techniques are employed before supervised machine learning algorithms such as clustering. They focus on finding the set of features that maximize clustering quality measures [16; 17]. When some subsets of data are labeled but others are not, semi-supervised feature selection techniques are used [18]. Supervised feature selection methods are used in conjunction with supervised machine learning algorithms such as classification or regression, where the decision or class variable is present. These techniques attempt to select a group of features that maximize the accuracy of the predicted decision variable using predefined supervised techniques [14; 19]. They require the availability of labeled data. Based on the selection methodologies, supervised feature selection algorithms are further classified into three types: wrapper, filter, and hybrid [20]. Figure 1: The Taxonomy of Features Selection Techniques-Supervised. Wrapper feature selection methods make use of optimization algorithms to choose the set of features that improve the classification accuracy of a predefined learning algorithm [21]. However, filter feature selection algorithms choose features based on their intrinsic characteristics [22]. On the other hand, hybrid feature selection methods combine the advantages of both filter and wrapper feature selection algorithms. They often consist of two phases. In the first phase, they employ either a filter or a wrapper technique as the initial step for minimizing a set of features. while another type of feature selection algorithm is used in the next phase to select the final set of attributes [23]. Filter feature selection methods are widely used in the literature because they are usually fast and scalable techniques [23]. This paper is concerned with supervised feature selection techniques due to the availability of class labels for human activities. It focuses on filter-based feature selection techniques to choose the list of the most relevant features for recognizing human activities. There are three types of filter feature selection techniques: statistical and informational, bioinspired, and spectral based. The first group makes use of statistical and/or information measures such as variance, entropy, and mutual information among features. Bio-inspired techniques employ bio-inspired stochastic optimization search strategies for choosing a good subset of features based on some quality criteria. However, the third group makes use of spectral analysis to find the best representative features [24].