Abstract The existing online streaming feature selection algorithms usually select the optimal global feature subset, and it is assumed that this subset adapts to all regions of the sample space. However, each region of the sample space is characterized accurately by its own distinct feature subsets. The feature subsets are likely to be different in feature and size. Therefore, an algorithm of local online streaming feature selection based on max-decision boundary is proposed. The local feature selection is introduced. With the full usage of local information, feature measurement standards based on max-decision boundary are designed to separate samples of the same class from samples of different classes as far as possible. Meanwhile, three strategies, maximizing average decision boundary, maximizing decision boundary and minimizing redundancy, are employed to select appropriate features. The class similarity measurement method is applied after the optimal feature subset is selected for the local regions. Experimental results and statistical hypothesis tests on fourteen datasets demonstrate the effectiveness and stability of the proposed algorithm.
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