A Greedy Performance Driven Algorithm for Decision Fusion Learning

We propose a greedy performance driven algorithm for learning how to fuse across multiple classification and search systems. We assume a scenario when many such systems need to be fused to generate the final ranking. The algorithm is inspired from Ensemble Learning but takes that idea further for improving generalization capability. Fusion learning is applied to leverage text, visual and model based modalities for 2005 TRECVID query retrieval task. Experiments using the well established retrieval effectiveness measure of mean average precision reveal that our proposed algorithm improves over naive baseline (fusion with equal weights) as well as over Caruana's original algorithm (NACHOS) by 36% and 46% respectively.