A Classifier Ensemble Framework for Multimedia Big Data Classification

Numerous classification algorithms have been developed for a variety of data types. However, it is nearly impossible for one classifier to perform the best in all kinds of datasets. Therefore, ensemble learning models which aim to take advantages of different classifiers have received a lot of attentions recently. In this paper, a scalable classifier ensemble framework assisted by a set of judgers is proposed to integrate the outputs from multiple classifiers for multimedia big data classification. Specifically, based on the confusion matrices of different classifiers, a set of "judgers" are organized into a hierarchically structured decision model. A testing instance is first input to different classifiers, and then the classification results are passed to the proposed hierarchical structured decision model to derive the final result. The ensemble system can be run on Spark, which is designed for big data processing. Experimental results on multimedia data containing different actions demonstrate that the proposed classifier ensemble framework outperforms several state-of-the-art model fusion approaches.