We address the problem of complicated event categorization from a large dataset of videos "in the wild", where multiple classifiers are applied independently to evaluate each video with a 'likelihood' score. The core contribution of this paper is a local expert forest model for meta-level score fusion for event detection under heavily imbalanced class distributions. Our motivation is to adapt to performance variations of the classifiers in different regions of the score space, using a divide-and-conquer technique. We propose a novel method to partition the likelihood-space, being sensitive to local label distributions in imbalanced data, and train a pair of locally optimized experts each time. Multiple pairs of experts based on different partitions ('trees') form a 'forest', balancing local adaptivity and over-fitting of the model. As a result, our model disregards classifiers in regions of the score space where their performance is bad, achieving both local source selection and fusion. We experiment with the TRECVID Multimedia Event Detection (MED) dataset, detecting 15 complicated events from around 34k video clips comprising more than 1000 hours, and demonstrate superior performance compared to other score-level fusion methods.