Online Feature Selection And Its Application

Compared to the traditional online knowledge discovery with a static feature space,online knowledge discovery with a dynamic feature space has not attracted much attention.A feature space is dynamic when not all features are available before learning begins or when the feature space changes dynamically over time.Therefore,a dynamic feature space might make the feature space of training data become high dimensional and uncertain,which is challenging for traditional online knowledge discovery algorithms.This thesis carries out analysis and studies aiming at the online streaming feature selection and feature selection with a dynamic feature space.Since the emergence of a new application scenario with both historical data and streaming features,an online feature selection based on feature clustering ensemble technology and streaming features was proposed.To compensate for the shortcomings of single clustering algorithm,the idea of clustering ensemble was introduced in the group feature selection of historical data.Firstly,the k-means clustering method was used to get multiple clustering and then the hierarchical clustering algorithm was used to integrate and obtain the final results.In the online feature selection phase of the streaming features,the feature groups were updated by investigating the feature correlations of feature groups generated in the group formation.The final feature subset was obtained by group transformation.The experimental results show that the proposed algorithm can effectively deal with the online feature selection problem in new scenario,and has good classification performance.Besides,an online learning method which towards mining trapezoidal data streams was introduced.The algorithm and the proposed algorithm were applied to feature selection of the streaming social media data.The application results show that these two algorithms can effectively deal with online feature selection and online learning of trapezoidal data streams in different scenarios of social media.