This study proposes a wrapper method based on a quantum-inspired evolutionary algorithm for feature selection in supervised classification. Firstly, it analyzes the shortcoming of excessively preferring classification accuracy in existing subset evaluation methods and then puts forward two new subset evaluation methods respectively based on a fixed threshold and a statistical test. Second, some improvements are made to the evolutionary strategy of the quantum-inspired evolutionary algorithm. More specifically, its whole evolutionary process is divided into two phases, in which individual and global extrems are selected as the evolutionary target of population respectively. On this basis, a feature selection algorithm is designed in accordance with the general wrapper framework. Finally, 15 UCI datasets are used to validate the effectiveness of the subset evaluation methods and the evolutionary strategy, as well as the superiority of the proposed method over other 6 feature selection methods. The results show that the new wrapper method achieves similar or even better classification accuracy in more than 80% of the datasets and selects feature subset with less number of features in 86.67% of the datasets.