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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| Published in: | Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 1524 - 1531 |
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11.10.2020
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| Abstract | 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 selection is also a time-consuming problem considering a large dataset it involves. The computation time can be easily reduced by introducing the parallelization into MOEA/D-STAT, thanks to the decomposition idea of MOEA/D. To the best of our knowledge, this is the first attempt to implement the parallelization of MOEA/D-STAT for feature selection. In this paper, we consider both master-slave models and island models, which are two different approaches of parallelization. In the master-slave models, different offspring assignment mechanisms are considered. In the island models, different island size specification mechanisms are examined. Our experimental results show that the master-slave models can achieve higher speedup and better performance than the island models. |
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| AbstractList | 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 selection is also a time-consuming problem considering a large dataset it involves. The computation time can be easily reduced by introducing the parallelization into MOEA/D-STAT, thanks to the decomposition idea of MOEA/D. To the best of our knowledge, this is the first attempt to implement the parallelization of MOEA/D-STAT for feature selection. In this paper, we consider both master-slave models and island models, which are two different approaches of parallelization. In the master-slave models, different offspring assignment mechanisms are considered. In the island models, different island size specification mechanisms are examined. Our experimental results show that the master-slave models can achieve higher speedup and better performance than the island models. |
| Author | Shang, Ke Liao, Weiduo Ishibuchi, Hisao Meng Pang, Lie |
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| Snippet | In machine learning field, feature selection can be treated as a bi-objective optimization problem. It is reported that a decomposition-based evolutionary... |
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| SubjectTerms | 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 |
| Title | Parallel Implementation of MOEA/D with Parallel Weight Vectors for Feature Selection |
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