Murali, Gaurav Pandey (2020): Integrating multimodal data through interpretable heterogeneous ensembles,īioRxiv. Yan-Chak Li, Linhua Wang, Jeffrey Law, T. More details of EI can be found in our Biorxiv preprint: The output is a set of compressed CSV files containing the class distribution produced by each classifier that serves as input to a later ensemble learning phase. EI was developed to support research by Yan-Chak Li, Linhua Wang, and Gaurav Pandey.ĮI is designed for generating extremely large ensembles (taking days or weeks to generate) and thus consists of an initial data generation phase tuned for multicore and distributed computing environments. Though other tools exist, we are unaware of a similarly modular, scalable pipeline designed for large-scale ensemble learning. It also fairly evaluates the performance of several ensemble learning methods including ensemble selection, and stacked generalization (stacking). Ensemble Integration (EI): Integrating multimodal data through interpretable heterogeneous ensemblesĮnsemble Integration (EI) is a customizable pipeline for generating diverse ensembles of heterogeneous classifiers, as well as the accompanying metadata needed for ensemble learning approaches utilizing ensemble diversity for improved performance.
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