Evolutionary Streaming Feature Selection via Incremental Feature Clustering
提出一种三阶段进化流式特征选择方法，通过增量聚类处理动态到达的特征组，在18个真实数据集上分类性能优于现有方法。
In many real-world scenarios, features could arrive in groups over time, and the total size of the feature space is often unknown. Streaming feature selection is a commonly-used approach to addressing such dynamic scenarios, where newly arriving features must be assessed for both their relevance and redundancy with previously selected features. To effectively solve such a task, this paper proposes an evolutionary streaming feature selection method via incremental feature clustering that comprises three stages. First, the online irrelevant feature filtering stage eliminates irrelevant streaming features to reduce the noise effect and shrink the search space. Second, the incremental redundant feature clustering stage groups mutually redundant features into clusters, adaptively creating or merging feature clusters to adapt to the dynamically-changing feature sets. Finally, the interactive feature subset search stage identifies representative features within each cluster to form the best feature subset. Experimental results on 18 real-world datasets demonstrate that the proposed method has better classification performance than the compared state-of-the-art methods.