Abstract：In data-intensive domains like medical diagnosis and financial risk control, feature selection is faced with severe challenges due to high-dimensional imbalanced data, including majority class bias, insufficient capture of high-order nonlinear feature interactions, and difficulty in collaborative optimization of conflicting multi-objectives. Traditional methods struggle to simultaneously achieve imbalanced data adaptation, high-order feature correlation mining, and multi-objective global optimization. Therefore, for supervised feature selection scenarios, a deep learning based multi-objective multi-task evolutionary approach for imbalanced feature selection(DLME) is proposed and a three-stage modular collaborative optimization framework is established. First, dynamic oversampling and preliminary feature filtering are performed by combining adaptive sampling and SMOTE based on particle swarm optimization, thereby alleviating class distribution imbalance. Then, a deep neural network is embedded. High-order nonlinear correlations among features are learned from the balanced sample set. The feature subset is subsequently refined. Finally, a grey wolf optimizer based multi-task multi-objective optimization framework is constructed. This framework integrates NSGA-II-based Pareto front search and front knowledge transfer mechanism. The optimal feature subset balancing classification accuracy and sparsity is output. Experimental results on public datasets and domain-specific datasets demonstrate the outstanding superiority of DLME under extremely imbalanced and high-dimensional scenarios. DLME exhibits excellent multi-objective optimization performance and robustness, thereby providing an effective solution for feature selection tasks on high-dimensional imbalanced data.
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2026, Vol. 39 
