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A hybrid feature selection strategy which combines filter and wrapper type approaches is also proposed.
When transmitting data from one operator to others, a complex Pig data structure is wrapped into a unified tuple with one field of "Wrapper" type.
Regarding the applied feature selection procedures in this study, at first, a wrapper type technique was employed (sequential backward elimination SBE) using the random forest (RF) algorithm [ 16], which utilizes ensembles of decision trees.
In wrapper type methods, the FS is "wrapped" around a learning method: the usefulness of a feature subset is directly judged by the estimated accuracy of a trained classifier.
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Flue-cured, Maryland, cigar-binder, and wrapper types of tobacco are produced on sandy and sandy loam soil.
We demonstrated that the proposed method performed competitively when compared with other wrapper-type feature selection methods in terms of class-separability and recognition rate.
Therefore, a wrapper-type feature selection method, SVM-RFE, was introduced [ 8].
Combined with more efficient, wrapper-type of search algorithms, such network-guides feature selection approaches could be scaled-up in the future to enable extracting also larger sub-networks with improved predictive capability.
There are many existing feature selection methods and they can be roughly categorized into three types, filter, wrapper, and embedded methods.
Feature selection methods can be subdivided into at least three types: filter, wrapper and embedded (Yvan Saeys et al., A review of feature selection techniques in bioinformatics - Bioinformatics 2007).
Wrappers refer to any type of soft paperback binding, and library binding a more durable type of binding that libraries sometimes re-bind books in.
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