Concept drift poses a challenge in the field of data stream mining. Most existing online deep learning methods rely on a single objective, failing to sufficiently capture the latent feature information embedded in the data, which hinders rapid adaptation to distribution changes. To address these issues, this paper proposes a Multi-objective Joint Optimization of Deep Network (MJOD) model. Specifically, the feature connection network (FCN) connects features from different layers via dense feature units, constructing an integrated classification objective to fully utilize both shallow and deep information for precise prediction. Subsequently, the feature refinement network (FRN) compares historical representative information with current data to build a compressed reconstruction objective, thereby capturing changes in data distribution. Furthermore, the feature enhancement network (FEN) extracts spatial structure information from the data and constructs an information balance objective to retain the information most relevant to the task. By jointly optimizing the integrated classification, compressed reconstruction, and information balance objectives, the model maximizes the utilization of feature information within the streaming data, enabling rapid learning of new distributions. Experimental results demonstrate that MJOD is superior to other baseline methods across various drift scenarios.

Class overlap is a major factor of data complexity that hampers classifier performance, particularly in imbalanced learning scenarios. Most existing oversampling methods rely on conservative seed sample selection and decoupled synthesis strategies, which limit sample diversity and fail to effectively control overlap risk. This paper proposes a novel oversampling framework called TMACO (Class Alliance-Constrained Oversampling), which integrates data complexity considerations into both seed selection and sample generation. First, TMACO selects seed sample units using a class alliance constraint that jointly considers spatial geometry and class distribution to enhance diversity and representativeness. Second, it generates synthetic samples based on three-point units to ensure regional stability. Third, a region-level filtering mechanism is applied to prevent synthetic samples from intruding into majority class areas. Extensive experiments on benchmark and real-world datasets demonstrate that TMACO consistently improves minority class performance and overall classification accuracy compared to state-of-the-art oversampling techniques. The proposed method also offers interpretable parameter control and adapts well to varying task objectives.

Concept drift is an important characteristic and inevitable difficult problem in streaming data mining. Ensemble learning is commonly used to deal with concept drift. However, most ensemble methods cannot balance the accuracy and diversity of base learners after drift occurs, and cannot adjust adaptively according to the drift type. To solve these problems, this paper proposes a targeted ensemble learning (Targeted EL) method to improve the accuracy and diversity of ensemble learning for streaming data with abrupt and gradual concept drift. First, to improve the accuracy of the base learners, the method adopts different sample weighting strategies for different types of drift to realize bidirectional transfer of new and old distributed samples. Second, the difference matrix is constructed by the prediction results of the base learners on the current samples. According to the drift type, the submatrix with appropriate size and maximum difference sum is extracted adaptively to select appropriate, accuracy and diverse base learners for ensemble. The experimental results show that the proposed method can achieve good generalization performance when dealing with the streaming data with abrupt and gradual concept drift.