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- W2895365144 abstract "The purpose of instance selection is to reduce the data size while preserving as much useful information stored in the data as possible and detecting and removing the erroneous and redundant information. In this work, we analyze instance selection in regression tasks and apply the NSGA-II multi-objective evolutionary algorithm to direct the search for the optimal subset of the training dataset and the k-NN algorithm for evaluating the solutions during the selection process. A key advantage of the method is obtaining a pool of solutions situated on the Pareto front, where each of them is the best for certain RMSE-compression balance. We discuss different parameters of the process and their influence on the results and put special efforts to reducing the computational complexity of our approach. The experimental evaluation proves that the proposed method achieves good performance in terms of minimization of prediction error and minimization of dataset size." @default.
- W2895365144 created "2018-10-12" @default.
- W2895365144 creator A5019094442 @default.
- W2895365144 creator A5078689403 @default.
- W2895365144 date "2018-09-29" @default.
- W2895365144 modified "2023-10-17" @default.
- W2895365144 title "Multi-Objective Evolutionary Instance Selection for Regression Tasks" @default.
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- W2895365144 doi "https://doi.org/10.3390/e20100746" @default.
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