The increasing overfitting risk when the number of observations is insufficient. The significant computation time when the number of variables is large. Embedded method Embedded methods have been recently proposed that try to combine the advantages of both previous methods. A learning algorithm takes advantage of its own variable selection process and performs feature selection and classification simultaneously, such as the FRMT algorithm. Application of feature selection metaheuristics This is a survey of the application of feature selection metaheuristics lately used in the literature. This survey was realized by J. Hammon in her 2013 thesis. Feature selection embedded in learning algorithms Some learning algorithms perform feature selection as part of their overall operation. These include: -regularization techniques, such as sparse regression, LASSO, and -SVM Regularized trees, e.g. regularized random forest implemented in the RRF package Decision tree Memetic algorithm Random multinomial logit (RMNL) Auto-encoding networks with a bottleneck-layer Submodular feature selection Local learning based feature selection. Compared with traditional methods, it does not involve any heuristic search, can easily handle multi-class problems, and works for both linear and nonlinear problems. It is also supported by a strong theoretical foundation. Numeric experiments showed that the method can achieve a close-to-optimal solution even when data contains >1M irrelevant features. Recommender system based on feature selection. The feature selection methods are introduced into recommender system research. See also Cluster analysis Data mining Dimensionality reduction Feature extraction Hyperparameter optimization Model selection Relief (feature selection) References Further reading External links Feature Selection Package, Arizona State University (Matlab Code) NIPS challenge 2003 (see also NIPS) Naive Bayes implementation with feature selection in Visual Basic (includes executable and source code) Minimum-redundancy-maximum-relevance (mRMR) feature selection program FEAST (Open source Feature Selection algorithms in C and MATLAB) Model selection Dimension reduction