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- W4285139526 abstract "Ensemble learning (EL) is a paradigm, involving several base learners working together to solve complex problems. The performance of the EL highly relies on the number and accuracy of weak learners, which are often hand-crafted by domain knowledge. Unfortunately, such knowledge is not always available to interested end-user. This paper proposes a novel approach to automatically select optimal type and number of base learners for disease classification, called Multi-Objective Evolutionary Ensemble Learning (MOE-EL). In the proposed MOE-EL algorithm, a variable-length gene encoding strategy of the multi-objective algorithm is first designed to search for the weak learner optimal configurations. Moreover, a dynamic population strategy is proposed to speed up the evolutionary search and balance the diversity and convergence of populations. The proposed algorithm is examined and compared with 5 existing algorithms on disease classification tasks, including the state-of-the-art methods. The experimental results show the significant superiority of the proposed approach over the state-of-the-art designs in terms of classification accuracy rate and base learner diversity." @default.
- W4285139526 created "2022-07-14" @default.
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- W4285139526 date "2022-01-01" @default.
- W4285139526 modified "2023-10-16" @default.
- W4285139526 title "Multi-objective Evolutionary Ensemble Learning for Disease Classification" @default.
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- W4285139526 doi "https://doi.org/10.1007/978-3-031-09677-8_41" @default.
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