Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing

作者：Wenjie Liu, Junxiu Chen, Yuxiang Wang, Peipei Gao, Zhibin Lei, Xu Ma · 发表于：Complexity · 年份：2020 · DOI：10.1155/2020/8216874 · 被引用次数：13 · 研究领域：Machine Learning in Materials Science、Quantum Computing Algorithms and Architecture、Advanced Memory and Neural 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 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 R y , 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 O M , 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 O M . Finally, in orde...