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An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes a feature selection method to identify key quality features in complex manufacturing processes. A multi-objective binary particle swarm optimization algorithm is proposed, which includes three new components to optimize a bi-objective feature selection model. Experimental results show that this method can identify a small number of key quality features with good predictive ability.
INFORMATION SCIENCES
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Computer Science, Interdisciplinary Applications
Zhen He, Hao Hu, Min Zhang, Yang Zhang, An-Da Li
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Abdolreza Rashno, Milad Shafipour, Sadegh Fadaei
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KNOWLEDGE-BASED SYSTEMS
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Rohit Kundu, Rammohan Mallipeddi
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(2022)
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Peng Wang, Bing Xue, Jing Liang, Mengjie Zhang
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INFORMATION SCIENCES
(2023)
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Computer Science, Artificial Intelligence
Pradip Dhal, Chandrashekhar Azad
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APPLIED SOFT COMPUTING
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Computer Science, Artificial Intelligence
Jianfeng Qiu, Xiaoshu Xiang, Chao Wang, Xingyi Zhang
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Azar Rafie, Parham Moradi, Abdulbaghi Ghaderzadeh
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Babak Nouri-Moghaddam, Mehdi Ghazanfari, Mohammad Fathian
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Rajesh Ranjan, Jitender Kumar Chhabra
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APPLIED SOFT COMPUTING
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Yu Xue, Haokai Zhu, Jiayu Liang, Adam Slowik
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Amal Francis Ukken, Arjun Bindu Jayachandran, Jaideep Kumar Punnath Malayathodi, Pranesh Das
Summary: Feature selection is an important step in improving the performance of a classifier. The Binary Multi-Objective Grey Wolf Optimizer-Sigmoid (BMOGWO-S) algorithm has been introduced as a better approach to feature selection compared to other existing algorithms. This algorithm utilizes a metaheuristic method and does not rely on statistical information from the dataset. In this research, the authors propose a Statistically aided Binary Multi-Objective Grey Wolf Optimizer-Sigmoid (SaBMOGWO-S) algorithm that incorporates statistical information and introduces methods to reduce computation time.
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APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Zhen He, Hao Hu, Min Zhang, Yang Zhang, An-Da Li
Summary: The paper proposes a data-driven method to effectively identify key quality characteristics in production processes, utilizing a multi-objective feature selection approach of maximizing geometric mean and minimizing the number of selected features. Experimental results show that the method exhibits good search performance on four production datasets.
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Article
Computer Science, Information Systems
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes a feature selection method to identify key quality features in complex manufacturing processes. A multi-objective binary particle swarm optimization algorithm is proposed, which includes three new components to optimize a bi-objective feature selection model. Experimental results show that this method can identify a small number of key quality features with good predictive ability.
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Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
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Georgios Charizanos, Haydar Demirhan, Duygu Icen
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