HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
情報科学研究科  >
雑誌発表論文等  >

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

pairwisednn_pakdd (2).pdf430.18 kBPDF見る/開く

タイトル: A Deep Neural Network for Pairwise Classification : Enabling Feature Conjunctions and Ensuring Symmetry
著者: Atarashi, Kyohei 著作を一覧する
Oyama, Satoshi 著作を一覧する
Kurihara, Masahito 著作を一覧する
Furudo, Kazune 著作を一覧する
発行日: 2017年
出版者: Springer
引用: 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
誌名: Lecture Notes in Computer Science
巻: 10234
開始ページ: 83
終了ページ: 95
出版社 DOI: 10.1007/978-3-319-57454-7_7
抄録: 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
資料タイプ: article (author version)
出現コレクション:雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

提供者: 小山 聡


本サイトに関するご意見・お問い合わせは repo at へお願いします。 - 北海道大学