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We consider the binary classification problem of static and dynamic mixed data in this paper. Different from mixed categorical and numerical data, the dynamic variables in the new type of data vary with time and are recorded at discrete time points. This discrete form results in the high correlation within each variable, at the same time, more shape and dynamic information need to be explored, then an efficient fusion model is urgently needed. To tackle the challenge, we propose a novel fusion method, where the discrete observations from dynamic variables are transformed to continuous functions via basis expansion, and then are combined with static variables via a hybrid logistic regression model, with a group lasso penalty term to select the important features. Consequently, the proposed method makes full use of the correlation and dynamic information, then discards the useless information. It can be regarded as an efficient tool to do the classification. In addition, two numerical examples and a real dataset are utilized to further illustrate the effectiveness of the proposed method.

An Advanced Hybrid Logistic Regression Model for Static and Dynamic Mixed Data Classification