  Dataset complexity assessment aims to predict classification performance on a
dataset with complexity calculation before training a classifier, which can
also be used for classifier selection and dataset reduction. The training
process of deep convolutional neural networks (DCNNs) is iterative and
time-consuming because of hyperparameter uncertainty and the domain shift
introduced by different datasets. Hence, it is meaningful to predict
classification performance by assessing the complexity of datasets effectively
before training DCNN models. This paper proposes a novel method called
cumulative maximum scaled Area Under Laplacian Spectrum (cmsAULS), which can
achieve state-of-the-art complexity assessment performance on six datasets.
