Inducer selection principles for deepfusion systems

The current landscape of ensemble learning or late fusion approaches is dominated by methods that employ a very low number of inducer sys-tems, while using traditional approaches with regards to the fusion engine, predominantly statistical, weighted, Bagging or Random Forests. Even with the advent of deep learning, few approaches use deep neural networks in building the ensemble decision and improving the results of single-system approaches. One of these methods is represented by the DeepFusion set of approaches, that integrate a very large number of inducer systems, while providing significantly improved final performance over the performance of its component inducers. However, no attempt has yet been made for Deep -Fusion with regards to reducing and optimizing the set of inducers, while maintaining the same level of performance. Thus, this paper proposes a set of methods for inducer selection and reduction, based on their performance and on their similarity computed via clustering. Our methods are tested on the popular Interestingness10k dataset, that provides data and inducers for the prediction of image and video visual interestingness. We present an in-depth analysis of the performance of the optimization methods, with regards to the results according to the main performance metric associated with this dataset, as well as the degree to which these methods reduce the number of utilized inducers.