A Comparison of Multi-Label Feature Selection Methods Using the Random Forest Paradigm

Ouadie Gharroudi 1 Haytham Elghazel 1 Alex Aussem 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this paper, we discuss three wrapper multi-label feature selection methods based on the Random Forest paradigm. These variants differ in the way they consider label dependence within the feature selection process. To assess their performance, we conduct an extensive experimental comparison of these strategies against recently proposed approaches using seven benchmark multi-label data sets from different domains. Random Forest handles accurately the feature selection in the multi-label context. Surprisingly, taking into account the dependence between labels in the context of ensemble multi-label feature selection was not found very effective.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01301070
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Submitted on : Monday, April 11, 2016 - 4:29:14 PM
Last modification on : Wednesday, November 20, 2019 - 2:59:33 AM

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  • HAL Id : hal-01301070, version 1

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Ouadie Gharroudi, Haytham Elghazel, Alex Aussem. A Comparison of Multi-Label Feature Selection Methods Using the Random Forest Paradigm. Canadian Conference on Artificial Intelligence, AI, May 2014, Montréal, Canada. pp.95-106. ⟨hal-01301070⟩

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