Effect of Metalearning on Feature Selection Employment

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

das Dôres, SN (Author) Other; Carlos Soares (Author) FEUP; Ruiz, DDA (Author) Other

Conference proceedings International

Title: AutoML@PKDD/ECML

Pages: 84-90

2017 International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms, AutoML 2017

22 September 2017

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00N-AQM

Abstract (EN): Feature Selection is important to improve learning performance, reduce computational complexity and decrease required storage. There are multiple methods for feature selection, with varying impact and computational cost. Therefore, choosing the right method for a given data set is important. In this paper, we analyze the advantages of metalearning for feature selection employment. This issue is relevant because a wrong decision may imply additional processing, when FS is unnecessarily applied, or in a loss of performance, when not used in a problem for which it is appropriate. Our results showed that, although there is an advantage in using metalearning, these gains are not yet sufficiently relevant, which opens the way for new research to be carried out in the area.

Language: English

Type (Professor's evaluation): Scientific