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. 2020 Aug 12;8:892. doi: 10.3389/fbioe.2020.00892

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Copyright © 2020 Li, Zhou, Wang, Wang and Li.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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The results of different experiments. (A) According to the results, this paper thought that the CT, GAAC, GDPC, GTPC, and TPC are not ideal. (B) According to the results, this paper thought that the greedy algorithm was more efficient than MRMD2.0. (C) According to the results, this paper thought that the greedy algorithm is worse than MRMD2.0 in the performance index of the selected model. (D) After several dimension reductions, the results showed that the MRMD2.0 was better than the greedy algorithm index of the selected model. (E) After several dimension reductions, the results showed that the dimension of model of the greedy algorithm is about five times that of the MRMD2.0. The results showed that as for the dimensions of the selected model, the greedy algorithm was more efficient than MRMD2.0. However, the greedy algorithm cannot further reduce the dimensions of the selected feature model, but MRMD2.0 can still further reduce it.