Considering the crucial influence of feature selection on data classification accuracy, a grey wolf optimizer based on quantum computing and uncertain symmetry rough set (QCGWORS) was proposed. QCGWORS was to apply a parallel of three theories to feature selection, and each of them owned the unique advantages of optimizing feature selection algorithm. Quantum computing had a good balance ability when exploring feature sets between global and local searches. Grey wolf optimizer could effectively explore all possible feature subsets, and uncertain symmetry rough set theory could accurately evaluate the correlation of potential feature subsets. QCGWORS intelligent algorithm could minimize the number of features while maximizing classification performance. In the experimental stage, k nearest neighbors (KNN) classifier and random forest (RF) classifier guided the machine learning process of the proposed algorithm, and 13 datasets were compared for testing experiments. Experimental results showed that compared with other feature selection methods, QCGWORS improved the classification accuracy on 12 datasets, among which the best accuracy was increased by 20.91%. In attribute reduction, each dataset had a benefit of the reduction effect of the minimum feature number.
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