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- W2966938530 abstract "Feature selection (FS) algorithm based on neural networks has been widely implemented due to its good nonlinear mapping ability and accurate approximation ability to arbitrary functions. However, the learning speed of the neural networks is very slow. To solve this problem, we propose a feature selection algorithm based on extreme learning machine (ELM), which determines the feature weights by analyzing the input weights and output weights, due to ELM randomly chooses the input weights and analytically determines the output weights. The proposed algorithm mainly considers the output weights and the feature weights are mainly determined by the output weights. According to the feature weights, many experiments are taken to obtain the optimal feature subsets for ELM. The results of several experiments demonstrate that by feature selection, the ELM achieves higher classification accuracy, and the training and testing process is much faster." @default.
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- W2966938530 date "2019-07-05" @default.
- W2966938530 modified "2023-10-16" @default.
- W2966938530 title "Feature Selection Based on Extreme Learning Machine" @default.
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- W2966938530 doi "https://doi.org/10.1145/3342999.3343017" @default.
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