document: Heterogeneous source consensus learning via decision propagation and negotiation Nowadays , enormous amounts of data are continuously generated not only in massive scale , but also from different , sometimes conflicting , views . Therefore , it is important to consolidate different concepts for intelligent decision making . For example , to predict the research areas of some people , the best results are usually achieved by combining and consolidating predictions obtained from the publication network , co-authorship network and the textual content of their publications . Multiple supervised and unsupervised hypotheses can be drawn from these information sources , and negotiating their differences and consolidating decisions usually yields a much more accurate model due to the diversity and heterogeneity of these models . In this paper , we address the problem of `` consensus learning '' among competing hypotheses , which either rely on outside knowledge ( supervised learning ) or internal structure ( unsupervised clustering ) . We argue that consensus learning is an NP-hard problem and thus propose to solve it by an efficient heuristic method . We construct a belief graph to first propagate predictions from supervised models to the unsupervised , and then negotiate and reach consensus among them . Their final decision is further consolidated by calculating each model 's weight based on its degree of consistency with other models . Experiments are conducted on 20 Newsgroups data , Cora research papers , DBLP author-conference network , and Yahoo ! Movies datasets , and the results show that the proposed method improves the classification accuracy and the clustering quality measure ( NMI ) over the best base model by up to 10 % . Furthermore , it runs in time proportional to the number of instances , which is very efficient for large scale data sets .
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