9/6/2023 0 Comments Jing software initial release![]() ![]() The joint prediction of a set of related objects. Generate label prediction for each individual, they provide useful constraints for Although unsupervised models, such as clustering, do not directly In this paper, we study ensemble learning with outputįrom multiple supervised and unsupervised models, a topic where little work hasīeen done. However, is limited in applications which have no access to raw data but to the To have improved accuracy and robustness over a single model. Ensemble classifiers such as bagging, boosting and model averaging are known Solution by maximizing the consensus among both supervised predictionsĪnd unsupervised constraints. (BGCM) refers to the method that consolidates a classification ![]() We collected the data for twenty US cities over a month.īipartite Graph-based Consensus Maximization To get ground truths, we crawl the true weather information forĮach day. High temperature, low temperature and weather condition,Īmong which the first two are continuous and the last is categorical. For each source, we collected data of three properties: Of three different days as three different sources, so altogether thereĪre nine sources. We integrate weather forecasting data collectedįrom three platforms: Wunderground, HAM weather, and To characterize different data types and weight distributions effectively.Ī good test bed for the task of integrating multiple sources of heterogeneous data. Is its ability of taking various loss and regularization functions Including the computation of truths and source weights as a Is to minimize the overall weighted deviation between the truthsĪnd the multi-source observations where each source is weighted by Reliability are defined as two sets of unknown variables. Problem using an optimization framework where truths and source Of source reliability has to be made by modeling multiple properties Moreover, each source possesses a variety When deriving the truths, but it is usually unknown which one is It is intuitive to trust reliable sources more Is to identify the true information (i.e., the truths) among conflicting Objects or events from a variety of sources. In many applications, one can obtain descriptions about the same Heterogeneous data (CRH) refers to the method that resolves conflictsĪmong multiple sources of heterogeneous data types. ![]()
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