Titlebook: Recent Advances in Ensembles for Feature Selection; Verónica Bolón-Canedo,Amparo Alonso-Betanzos Book 2018 Springer International Publishi

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Evaluation of Ensembles for Feature Selection,der to devise more powerful ensembles. Section?. comments on the stability of feature selection ensembles and Sect.?. defines performance evaluation measures for both subsets of features and rankings of features. Finally, Sect.?. summarizes and discusses the contents of this chapter.

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Tidious

Emerging Challenges,ld, in an attempt to obtain better performances and also design distributed FS schemes that allow for more effective process and higher efficiencies. This chapter outlines some of the latest challenges in the field of ensemble feature selection, aiming researchers at following the new paths that are

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Book 2018f successful applications of ensembles for FS and introduces the new challenges thatresearchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining.?.

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Ensembles for Feature Selection,o illustrate the concepts, in Sects.?. and .. Finally, in Sect.?., a brief comparison between the results obtained by both approaches employed in the use cases is shown, with the aim of giving the readers a brief guideline of their better use.

妨礙

Combination of Outputs,ictions, subsets of features or rankings of features. In this chapter we will describe methods falling in all these categories, so that the interesting readers can make an informed choice according to their needs trying to design the best ensemble possible.

Incommensurate

EXUDE

Feature Selection,tures of the data. In this scenario, the importance of feature selection is beyond doubt and different methods have been developed, although researchers do not agree on which one is the best method for any given setting. This chapter provides the reader with the foundations about feature selection (

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