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To address the trade-off problem of quality-diversity for the generatedimages in imbalanced classification tasks, we research on over-sampling basedmethods at the feature level instead of the data level and focus on searchingthe latent feature space for optimal distributions. On this basis, we proposean iMproved Estimation Distribution Algorithm based Latent featUre DistributionEvolution (MEDA_LUDE) algorithm, where a joint learning procedure is programmedto make the latent features both optimized and evolved by the deep neuralnetworks and the evolutionary algorithm, respectively. We explore the effect ofthe Large-margin Gaussian Mixture (L-GM) loss function on distribution learningand design a specialized fitness function based on the similarities amongsamples to increase diversity. Extensive experiments on benchmark basedimbalanced datasets validate the effectiveness of our proposed algorithm, whichcan generate images with both quality and diversity. Furthermore, the MEDA_LUDEalgorithm is also applied to the industrial field and successfully alleviatesthe imbalanced issue in fabric defect classification.

Distribution Learning Based on Evolutionary Algorithm Assisted Deep Neural Networks for Imbalanced Image Classification