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Dynamic Reliable Voting in Ensemble Learning

Agus Budi Raharjo 1 Mohamed Quafafou 1 
1 DANA - Data Mining at scale
LIS - Laboratoire d'Informatique et Systèmes
Abstract : The combination of multiple classifiers can produce an optimal solution than relying on the single learner. However, it is difficult to select the reliable learning algorithms when they have contrasted performances. In this paper, the combination of the supervised learning algorithms is proposed to provide the best decision. Our method transforms a classifier score of training data into a reliable score. Then, a set of reliable candidates is determined through static and dynamic selection. The experimental result of eight datasets shows that our algorithm gives a better average accuracy score compared to the results of the other ensemble methods and the base classifiers.
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Agus Budi Raharjo, Mohamed Quafafou. Dynamic Reliable Voting in Ensemble Learning. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.178-187, ⟨10.1007/978-3-030-19823-7_14⟩. ⟨hal-02331314⟩

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