基于自适应密度邻域关系的多标签在线流特征选择OACSTPCD

Stream feature selection selects the optimal feature subset from the feature data arriving in the form of stream.Most existing methods require prior learning of domain information and presetting of given parameter values during model training.In real-world appli-cations,due to the differences in data structure and source,researchers cannot obtain relevant domain information in advance during the model learning process for different datasets,and it is a huge challenge for them to specify a unified parameter for different types of datasets.Motivated by this,we propose a multi-label online stream feature selection based on adaptive density neighborhood relation(ML-OFS-ADNR).On the basis of the neighborhood rough set theory,the proposed method does not require any prior domain information in feature dependency calculation.Moreover,a new adaptive density neighborhood relationship is proposed,which can auto-matically select an appropriate number of neighborhoods in the streaming feature selection process using the density information of surrounding instances,and there is no need to specify any parameters in advance.By the fuzzy equal constraint,ML-OFS-ADNR can select features with high dependency and low redundancy.Experimental studies on ten different types of data sets show that the proposed method is superior to traditional feature selection methods with the same numbers of features and state-of-the-art online streaming feature selection algorithms in an online manner.