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Table 5 Description of feature selection methods used in machine learning[37]

From: A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli

Method

Description

Examples

Filter

Filter methods evaluate the relatedness of features by looking at the inherent properties of the data. Usually a feature relevance score is computed, and the features with low scores are discarded.

Student’s t-test [N/A]

Information gain [38]

Gain ratio [38]

Chi squared [N/A]

Symmetrical uncertainty [39]

Unbalanced correlation score [40]

Mann–Whitney test [41]

Linear correlation coefficient [N/A]

Wrapper

In wrapper methods various subsets of features are evaluated by training and testing a specific classification model, so a search algorithm is ‘wrapped’ around the classification model. This approach adapted to a specific classification algorithm.

Sequential forward selection [42]

Sequential backward elimination [42]

Beam search [43]

ReliefF [44]

Embedded

Embedded methods, build the search for an optimal subset of features into the classifier construction, so they are specific to a given learning algorithm.

Random forest [45]

SVM recursive feature elimination (SvmRfe) [46]

One attribute rule [47]