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Ensemble Feature Selection Algorithm
Author(s) -
Yassine Akhiat,
Mohamed Chahhou,
Ahmed Zinedine
Publication year - 2019
Publication title -
international journal of intelligent systems and applications
Language(s) - English
Resource type - Journals
eISSN - 2074-9058
pISSN - 2074-904X
DOI - 10.5815/ijisa.2019.01.03
Subject(s) - computer science , benchmark (surveying) , feature selection , feature (linguistics) , selection (genetic algorithm) , artificial intelligence , ensemble learning , ensemble forecasting , machine learning , pattern recognition (psychology) , algorithm , data mining , linguistics , philosophy , geodesy , geography
In this paper, we propose a new feature selection algorithm based on ensemble selection. In order to generate the library of models, each model is trained using just one feature. This means each model in the library represents a feature. Ensemble construction returns a well performing subset of features associated to the well performing subset of models. Our proposed approaches are evaluated using eight benchmark datasets. The results show the effectiveness of our ensemble selection approaches.

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