{"type":"ScholarlyArticle","title":"Integrating Specialized Classifiers Based on Continuous Time Markov Chain","authors":[{"name":"Zhizhong Li","slug":"zhizhong-li","given":"Zhizhong","family":"Li","affiliation":"The Chinese University of Hong Kong"},{"name":"Dahua Lin","slug":"dahua-lin","given":"Dahua","family":"Lin","affiliation":"The Chinese University of Hong Kong"}],"abstract":"Specialized classifiers, namely those dedicated to a subset of classes, are often adopted in real-world recognition systems. However, integrating such classifiers is nontrivial. Existing methods, e.g. weighted average, usually implicitly assume that all constituents of an ensemble cover the same set of classes. Such methods can produce misleading predictions when used to combine specialized classifiers. This work explores a novel approach. Instead of combining predictions from individual classifiers directly, it first decomposes the predictions into sets of pairwise preferences, treating them as transition channels between classes, and thereon constructs a continuous-time Markov chain, and use the equilibrium distribution of this chain as the final prediction. This way allows us to form a coherent picture over all specialized predictions. On large public datasets, the proposed method obtains considerable improvement compared to mainstream ensemble methods, especially when the classifier coverage is highly unbalanced.","doi":"10.24963/ijcai.2017/312","url":"https://ijcai.org/proceedings/2017/312","pdf":"https://ijcai.org/proceedings/2017/0312.pdf","markdown":"https://ijcai.org/proceedings/2017/312.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/312","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":312,"pages":{"first":2244,"last":2251},"keywords":["Machine Learning: Classification","Machine Learning: Deep Learning","Machine Learning: Ensemble Methods"],"videos":[],"references":[{"position":1,"raw":"[Breiman, 1996] Leo Breiman. 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