Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing
The complex systems with edge computing require a huge amount of multifeature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption. In this paper, a qua...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8216874 |
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| _version_ | 1832566296900796416 |
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| author | Wenjie Liu Junxiu Chen Yuxiang Wang Peipei Gao Zhibin Lei Xu Ma |
| author_facet | Wenjie Liu Junxiu Chen Yuxiang Wang Peipei Gao Zhibin Lei Xu Ma |
| author_sort | Wenjie Liu |
| collection | DOAJ |
| description | The complex systems with edge computing require a huge amount of multifeature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption. In this paper, a quantum-based feature selection algorithm for the multiclassification problem, namely, QReliefF, is proposed, which can effectively reduce the complexity of algorithm and improve its computational efficiency. First, all features of each sample are encoded into a quantum state by performing operations CMP and Ry, and then the amplitude estimation is applied to calculate the similarity between any two quantum states (i.e., two samples). According to the similarities, the Grover–Long method is utilized to find the nearest k neighbor samples, and then the weight vector is updated. After a certain number of iterations through the above process, the desired features can be selected with regards to the final weight vector and the threshold τ. Compared with the classical ReliefF algorithm, our algorithm reduces the complexity of similarity calculation from O(MN) to O(M), the complexity of finding the nearest neighbor from O(M) to OM, and resource consumption from O(MN) to O(MlogN). Meanwhile, compared with the quantum Relief algorithm, our algorithm is superior in finding the nearest neighbor, reducing the complexity from O(M) to OM. Finally, in order to verify the feasibility of our algorithm, a simulation experiment based on Rigetti with a simple example is performed. |
| format | Article |
| id | doaj-art-b6433cfcf28b4f50b9110ac283b35135 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b6433cfcf28b4f50b9110ac283b351352025-02-03T01:04:39ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/82168748216874Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge ComputingWenjie Liu0Junxiu Chen1Yuxiang Wang2Peipei Gao3Zhibin Lei4Xu Ma5Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaHong Kong Applied Science and Technology Research Institute (ASTRI), Hong Kong 999077, ChinaSchool of Software, Qufu Normal University, Shandong 273165, ChinaThe complex systems with edge computing require a huge amount of multifeature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption. In this paper, a quantum-based feature selection algorithm for the multiclassification problem, namely, QReliefF, is proposed, which can effectively reduce the complexity of algorithm and improve its computational efficiency. First, all features of each sample are encoded into a quantum state by performing operations CMP and Ry, and then the amplitude estimation is applied to calculate the similarity between any two quantum states (i.e., two samples). According to the similarities, the Grover–Long method is utilized to find the nearest k neighbor samples, and then the weight vector is updated. After a certain number of iterations through the above process, the desired features can be selected with regards to the final weight vector and the threshold τ. Compared with the classical ReliefF algorithm, our algorithm reduces the complexity of similarity calculation from O(MN) to O(M), the complexity of finding the nearest neighbor from O(M) to OM, and resource consumption from O(MN) to O(MlogN). Meanwhile, compared with the quantum Relief algorithm, our algorithm is superior in finding the nearest neighbor, reducing the complexity from O(M) to OM. Finally, in order to verify the feasibility of our algorithm, a simulation experiment based on Rigetti with a simple example is performed.http://dx.doi.org/10.1155/2020/8216874 |
| spellingShingle | Wenjie Liu Junxiu Chen Yuxiang Wang Peipei Gao Zhibin Lei Xu Ma Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing Complexity |
| title | Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing |
| title_full | Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing |
| title_fullStr | Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing |
| title_full_unstemmed | Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing |
| title_short | Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing |
| title_sort | quantum based feature selection for multiclassification problem in complex systems with edge computing |
| url | http://dx.doi.org/10.1155/2020/8216874 |
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