A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset.

Abstract

The Siamese Neural Network (SNN) is a neural network architecture capable of learning similarity knowledge between cases in a case base by receiving pairs of cases and analysing the differences between their features to map them to a multi-dimensional feature space. This paper demonstrates the development of a Convolutional Siamese Network (CSN) for the purpose of case similarity knowledge generation on the SelfBACK dataset. We also demonstrate a CSN is capable of performing classification on the SelfBACK dataset to an accuracy which is comparable with a standard Convolutional Neural Network.

Citation

MARTIN, K., WIRATUNGA, N., SANI, S., MASSIE, S. and CLOS, J. 2017. A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset. In Sanchez-Ruiz, A.A. and Kofod-Petersen, A. (eds.) Workshop proceedings of the 25th International conference on case-based reasoning (ICCBR 2017), 26-29 June 2017, Trondheim, Norway. CEUR workshop proceedings, 2028. Aachen: CEUR-WS [online], session 2: case-based reasoning and deep learning workshop (CBRDL-2017), pages 85-94. Available from: http://ceur-ws.org/Vol-2028/paper8.pdf

• Conference Name: 25th International conference on case-based reasoning (ICCBR 2017)
• Conference Location: Trondheim, Norway
• Start Date: Jun 26, 2017
• End Date: Jun 29, 2017
• Acceptance Date: May 25, 2017
• Online Publication Date: Jun 26, 2017
• Publication Date: Dec 18, 2017
• Deposit Date: Sep 4, 2017
• Publicly Available Date: Sep 4, 2017
• Print ISSN: 1613-0073
• Publisher: CEUR Workshop Proceedings
• Pages: 85-94
• Series Title: CEUR workshop proceedings
• Series Number: 2028
• Series ISSN: 1613-0073
• Keywords: Case based reasoning; Siamese neural networks; Categorisation; SelfBACK
• Public URL: http://hdl.handle.net/10059/2490
• Publisher URL: http://ceur-ws.org/Vol-2028/paper8.pdf