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Error-Correcting Output Codes (ECOC) is widely deployed to tackle the multiclass classification problem by reducing the original multi-class problem to several binary sub-problems. This study attempts to design a dynamic ensemble selection strategy to promote the performance of ECOC algorithms. Concretely, each column in a coding matrix is matched with a set of feature subsets generated by various feature selection methods. In the decoding process, a novel criterion based on the data complexity theory is proposed to pick up an optimal feature subset from the candidate subsets, so as to better distinguish unknown samples. As this strategy can be embedded in all types of ECOC algorithms, seven classical ECOC algorithms are deployed to verify the effectiveness of our strategy. Experiments are carried out on a set of UCI data sets, and the results confirm that despite different working principle, the proposed strategy can further improve the performance of various ECOC algorithms in most cases. Our python source code is available at: https://github.com/MLDMXM2017/ECOC_DES. [ABSTRACT FROM AUTHOR]