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• W2497373522 abstract "Extreme Learning Machine (ELM) and its variants have been widely used in many big data learning applications where raw data with imbalanced class distribution can be easily found. Although there have been several works solving the machine learning and robust regression problems using MapReduce framework, they need multi-iterative computations. Therefore, in this paper, we propose a novel Distributed Weighted Extreme Learning Machine based on MapReduce framework, named DWELM, which can learn the big imbalanced training data efficiently. Firstly, after indepth analyzing the properties of centralized Weighted ELM (WELM), it can be found out that the matrix multiplication operators in WELM are decomposable. Next, a DWELM based on MapReduce framework can be developed, which can first calculate the matrix multiplications effectively using two MapReduce Jobs in parallel, and then calculate the corresponding output weight vector with centralized computing. Finally, we conduct extensive experiments on synthetic data to verify the effectiveness and efficiency of our proposed DWELM in learning big imbalanced training data with various experimental settings." @default.
• W2497373522 created "2016-08-23" @default.
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• W2497373522 date "2016-01-01" @default.
• W2497373522 modified "2023-09-25" @default.
• W2497373522 title "Distributed Weighted Extreme Learning Machine for Big Imbalanced Data Learning" @default.
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• W2497373522 doi "https://doi.org/10.1007/978-3-319-28397-5_25" @default.
• W2497373522 hasPublicationYear "2016" @default.
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