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A Supervised Feature Selection Approach Based on Global Sensitivity

Sulieman, Hana; Alzaatreh, Ayman

Abstract:
In this paper we propose a wrapper method for feature selection in supervised learning. It is based on the global sensitivity analysis; a variancebased technique that determines the contribution of each feature and their interactions to the overall variance of the target variable. First-order and total Sobol sensitivity indices are used for feature ranking. Feature selection based on global sensitivity is a wrapper method that utilizes the trained model to evaluate feature importance. It is characterized by its computational efficiency because both sensitivity indices are calculated using the same Monte Carlo integral. A publicly available data set in machine learning is used to demonstrate the application of the algorithm.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000087327/03
Veröffentlicht am 06.08.2019
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Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000097155
Erschienen in Archives of Data Science, Series A (Online First)
Band 5
Heft 1
Seiten A03, 13 S. online
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