@inproceedings{4978c73fcbfa4f9ea91d1b4342c9ec66,
title = "Dynamic Reliable Voting in Ensemble Learning",
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.",
keywords = "Confidence score, Ensemble learning, Reliable voting",
author = "Raharjo, {Agus Budi} and Mohamed Quafafou",
note = "Publisher Copyright: {\textcopyright} 2019, IFIP International Federation for Information Processing.; 15th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2019 ; Conference date: 24-05-2019 Through 26-05-2019",
year = "2019",
doi = "10.1007/978-3-030-19823-7_14",
language = "English",
isbn = "9783030198220",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer New York LLC",
pages = "178--187",
editor = "Ilias Maglogiannis and John MacIntyre and Elias Pimenidis and Lazaros Iliadis",
booktitle = "Artificial Intelligence Applications and Innovations - 15th IFIP WG 12.5 International Conference, AIAI 2019, Proceedings",
}