## 2 Literature Review  
### Feature Selection  
Feature selection is the process of identifying a valuable subset of features from the original dataset to be used in building a model. Feature selection techniques aim to reduce the dimensionality of the feature space, eliminate redundant data, enhance data quality, improve model accuracy, and gain knowledge about the process that led to the data [13; 14; 15]. Three approaches for feature selection exist: supervised, unsupervised, and semi-supervised. Supervised techniques select relevant features based on labeled datasets using filter, wrapper, or embedded models. Filter models evaluate features independently of the classification model; wrapper models interact with the classification model during feature selection; and embedded models incorporate feature selection into classifier construction. Unsupervised techniques, such as Principal Component Analysis (PCA), eliminate irrelevant features through dimensionality reduction, considering the similarity or correlation between data. On the other hand, semi-supervised techniques, such as Denoising Autoencoder (DAE), utilize labeled and unlabeled data to assess the relevance of features, functioning effectively in scenarios where most of the data is labeled [13; 14]. There are several techniques for feature selection. Minimum Redundancy Maximum Relevance (mRMR), developed by [16], chooses a subset of features that contain valuable information for a specific task, minimizing redundancy among them and maximizing their relevance for the task in question. In contrast, Maximum Relevance evaluates the individual importance of each characteristic concerning the target variable. Minimum redundancy considers the correlation between characteristics and each other [8; 16]. Neighborhood Component Analysis (NCA) is a dimensionality reduction technique developed by [17], which learns a linear transformation of the original data so that the Euclidean distance in the new representation is meaningful for the classification task, transforming the data into a more discriminative feature space. Principal Component Analysis is a multivariate analysis technique developed by [18] designed to simplify complex datasets by reducing their dimensionality. The fundamental concept of PCA involves identifying the primary direction of variability within the data and representing it as a single principal component. Subsequently, PCA identifies additional orthogonal directions, each capturing subsequent levels of variability, creating subsequent principal components. This iterative process continues until the principal components encapsulate all significant variations in the data [18; 19; 20]. A Denoising Autoencoder (DAE) is an Artificial Neural Network used for unsupervised learning to acquire efficient data representations. Developed by [21], the technique aims to reduce the dimensionality of data by imposing a reconstruction constraint. To achieve this, DAE employs both an encoder and a decoder: the encoder compresses the input data while the decoder reconstructs it. During training, the DAE learns to map noisy input back to its original form, effectively capturing essential data features while filtering out noise. This process results in a robust network capable of handling noisy data [21]. Table 1 shows some techniques for feature selection, including the purpose and category (filter, wrapper, and embedded) to which each one belongs. In the filter category, there are methods such as Pearson correlation, which measure the linear relationship between variables and help to identify those with the most significant influence on the result. In the wrapper category, there are more sophisticated techniques, such as mRMR, Recursive Feature Elimination (RFE), and Greedy Forward Selection (GFS), which evaluate subsets of variables to find the best combination of features.