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- W2163776610 abstract "Networked data, extracted from social media, web pages, and bibliographic databases, can contain entities of multiple classes, interconnected through different types of links. In this paper, we focus on the problem of performing multi-label classification on networked data, where the instances in the network can be assigned multiple labels. In contrast to traditional content-only classification methods, relational learning succeeds in improving classification performance by leveraging the correlation of the labels between linked instances. However, instances in a network can be linked for various causal reasons, hence treating all links in a homogeneous way can limit the performance of relational classifiers." @default.
- W2163776610 created "2016-06-24" @default.
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- W2163776610 date "2013-08-11" @default.
- W2163776610 modified "2023-09-27" @default.
- W2163776610 title "Multi-label relational neighbor classification using social context features" @default.
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- W2163776610 doi "https://doi.org/10.1145/2487575.2487610" @default.
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