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A Deep Neural Network for Pairwise Classification : Enabling Feature Conjunctions and Ensuring Symmetry

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/65169

Title: A Deep Neural Network for Pairwise Classification : Enabling Feature Conjunctions and Ensuring Symmetry
Authors: Atarashi, Kyohei Browse this author
Oyama, Satoshi Browse this author →KAKEN DB
Kurihara, Masahito Browse this author →KAKEN DB
Furudo, Kazune Browse this author
Issue Date: 2017
Publisher: Springer
Citation: Advances in Knowledge Discovery and Data Mining : 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part I, ISBN: 978-3319574530
Journal Title: Lecture Notes in Computer Science
Volume: 10234
Start Page: 83
End Page: 95
Publisher DOI: 10.1007/978-3-319-57454-7_7
Abstract: Pairwise classification is a computational problem to determine whether a given ordered pair of objects satisfies a binary relation R which is specified implicitly by a set of training data used for ‘learning’ R. It is an important component for entity resolution, network link prediction, protein-protein interaction prediction, and so on. Although deep neural networks (DNNs) outperform other methods in many tasks and have thus attracted the attention of machine learning researchers, there have been few studies of applying a DNN to pairwise classification. Important properties of pairwise classification include using feature conjunctions across examples. Also, it is known that making the classifier invariant to the data order is an important property in applications with a symmetric relation R, including those applications mentioned above. We first show that a simple DNN with fully connected layers cannot satisfy these properties and then present a pairwise DNN satisfying these properties. As an example of pairwise classification, we use the author matching problem, which is the problem of determining whether two author names in different bibliographic data sources refer to the same person. We show that the method using our model outperforms methods using a support vector machine and simple DNNs.
Rights: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-57454-7_7
Type: article (author version)
URI: http://hdl.handle.net/2115/65169
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小山 聡

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