Parallel Implementation of MOEA/D with Parallel Weight Vectors for Feature Selection

In machine learning field, feature selection can be treated as a bi-objective optimization problem. It is reported that a decomposition-based evolutionary multi-objective optimization algorithm (i.e., MOEA/D-STAT) has good diversity performance when coping with feature selection. However, feature se...

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Veröffentlicht in:	Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics S. 1524 - 1531
Hauptverfasser:	Liao, Weiduo, Ishibuchi, Hisao, Meng Pang, Lie, Shang, Ke
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 11.10.2020
Schlagworte:	Computational modeling Cybernetics Feature extraction Feature selection island models Machine learning Machine learning algorithms Master-slave master-slave models MOEA/D-STAT Optimization parallel weight vectors parallelization
ISSN:	2577-1655
Online-Zugang:	Volltext
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