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Content Provider | ACM Digital Library |
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Author | McDowell, Luke K. Aha, David W. |
Abstract | Many classification tasks involve linked nodes, such as people connected by friendship links. For such networks, accuracy might be increased by including, for each node, the (a) labels or (b) attributes of neighboring nodes as model features. Recent work has focused on option (a), because early work showed it was more accurate and because option (b) fit poorly with discriminative classifiers. We show, however, that when the network is sparsely labeled, "relational classification" based on neighbor attributes often has higher accuracy than "collective classification" based on neighbor labels. Moreover, we introduce an efficient method that enables discriminative classifiers to be used with neighbor attributes, yielding further accuracy gains. We show that these effects are consistent across a range of datasets, learning choices, and inference algorithms, and that using both neighbor attributes and labels often produces the best accuracy. |
Starting Page | 847 |
Ending Page | 852 |
Page Count | 6 |
File Format | |
ISBN | 9781450322638 |
DOI | 10.1145/2505515.2505628 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2013-10-27 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Link-based classification Statistical relational learning Semi-supervised learning Social networks Collective inference |
Content Type | Text |
Resource Type | Article |
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