Dataset Complexity Assessment Based on Cumulative Maximum Scaled Area Under Laplacian Spectrum

However, training DCNN models requires a massive amount of computation time [28], and we cannot confirm test classification performance before the training process because of the uncertainty of DCNN models [13]. Because of the high correlation between the classification performance of DCNN models and the complexity of datasets, some complexity assessment methods have been proposed to solve the aforementioned problems [29]. By effectively evaluating a dataset's complexity in advance, we can estimate the classification performance of DCNN models trained on the dataset, saving a substantial amount of time [24]. Furthermore, complexity assessment methods can be used in certain applications (e.g., classifier selection [7] and dataset reduction [23]). Dataset complexity assessment methods aim to evaluate the entanglement degree of dataset classes.