Classification is one of the essential tasks in machine learning whose aim is to classify each instance of the dataset into different classes based on its features. It is often difficult to determine which features are useful without prior knowledge. As a result, a large number of features are usually introduced into the dataset, which may be irrelevant or redundant. Feature selection is a process of selecting a small subset of relevant features from a large original feature set. This small subset of features can have less redundant or relevant features, simplifying the machine learning process with reduced learning process time and increased performance. Other benefits of feature selection are improved prediction performance, scalability, understandability, and generalization ability of the classifier. It also reduces computational complexity and storage, provides faster and more cost-effective model and knowledge discovery. Additionally, it offers new insights to determine which features are most relevant or informative. The main challenge that arises during feature selection is the large search space where for n datasets the solutions are 2^n. Feature selection involves complex steps that are usually costly. And even the optimal model parameters of an entire feature set may need to be redefined several times in order to obtain the optimal model parameters for the selected feature subsets. Feature selection also involves two main goals, which are to maximize the classification accuracy and minimize the number of features, both of which are conflicting goals. Therefore, feature selection is considered a multi-objective problem with trade-off solutions lying between these two objectives. Some examples of feature selection techniques are information gain, chi-square, lasso, and Fisher score. Feature selection can be used to find key genes (i.e. biomarkers) among a large number of candidate genes in biological and biomedical problems, to discover indicators or basic features to describe the dynamic business environment, to select key terms such as words or phrases in text. mining and for choosing or constructing important visual contents such as pixels, color, texture and shape in image analysis. Compared to other dimensionality reduction techniques such as those based on e.g. projection, principal component analysis (PCA) or compression, feature selection techniques do not modify the original representation of the variables, but simply select a subset of them. Therefore, they retain the original semantics of the variables providing interpretability. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay Feature selection used on gene expression data that has a small sample size is called gene selection. Genetic selection can be used to find key genes arising from biological and biochemical problems. This type of feature selection is important for the detection and discovery of diseases such as.