The phrase "automatic feature selection" is correct and usable in written English.
It can be used in contexts related to data analysis, machine learning, or statistics, where it refers to the process of automatically selecting relevant features from a dataset.
Example: "In our study, we implemented automatic feature selection to improve the model's performance by reducing dimensionality."
Alternatives: "automated feature selection" or "automatic attribute selection".
Reference [31] investigates the use of a large open feature sets and automatic feature selection combined with support vector machines as classifiers.
In our comparative tests, the optimal feature sets for each classifier (the proposed algorithm, SVM and LDA) were chosen using the automatic feature selection procedure detailed in Section 2.6.
Decision tree induction algorithms such as C4.5 have incorporated in their learning phase an automatic feature selection strategy, while some other statistical classification algorithm require the feature subset to be selected in a preprocessing phase.
Right: RMSE score against different models built using the automatic feature selection algorithm Fig. 5 Box and whisker plots showing the output variable crossethnic (percentage of cross-ethnic marriages) for the four cities in the UK across two parameters love-radar and new-link-chance in the DITCH model (with box plots drawn across the three different values of love-radar).
This paper proposes an evolutionary approach to designing an SVM-based classifier (named ESVM) by simultaneous optimization of automatic feature selection and parameter tuning using an intelligent genetic algorithm, combined with k-fold cross-validation regarded as an estimator of generalization ability.
Furthermore, Genetic Programming is able to perform an automatic feature selection.
The proposed method can make use of different representations simultaneously (i.e., multiview learning) to obtain a better prediction performance than using a single feature representation (i.e., single-view learning) or a subset of features, with the advantage of automatic feature selections.
A semi-automatic feature selection was explored in the modeling phase, performed with the data prior to July 2012 and that allowed to select a reduced set of 22 features.
This paper introduces an ensemble method for classification that inherently provides automatic and interpretable feature selection.
Variational Bayesian (VB) backfitting is a fully automatic regression and feature selection method, where the only remaining hyperparameters are the initial values for the noise variances and the convergence criteria for the variational EM loop.
We propose a fully data driven forecasting methodology that combines filter and wrapper approaches for feature selection, including automatic feature evaluation, construction and transformation.
