大规模的红外光谱数据集中存在大量无关冗余的特征。针对这一问题，提出了一种动态赋权红外光谱特征选择算法(Dynamic Weight Infrared Spectrum Feature Selection Algorithm, MBDWFS)。 该算法把对称不确定性度量标准与近似Markov Blanket相结合，以删除原始光谱数据集中无关冗余的特征，从而获取数据规模较小且最优的特征子集。通过与 FCBF、ID3 和ReliefF三种经典特征选择算法的性能仿真对比试验，证明所提出的MBDWFS算法在整体分类性能上优于其他三种算法，用于红外光谱的物质分析领域时效果更好。

There exist a large number of irrelevant and redundant features in large-scale infrared spectrum datasets. To solve this problem, a dynamic weighted infrared spectrum feature selection algorithm (MBDWFS) is proposed. The algorithm deletes the irrelevant and redundant features in an original spectrum dataset by combining the symmetric uncertainty metrics with Markov Blanket. Then, a smaller scale optimal feature subset is obtained. By comparison with three classical feature selection algorithms FCBF, ID3 and ReliefF, it shows that the proposed MBDWFS algorithm is better than the above three algorithms in overall classification performance and is more suitable to be used in the field of material infrared spectrum analysis.