Rubio-Solis, A. and Panoutsos, G. (2016) Iterative Information Granulation for Novelty Detection in Complex Datasets. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2016 IEEE World Congress on Computational Intelligence, 24/07/2016-29/07/2016, Vancouver, Canada. Institute of Electrical and Electronics Engineers ISBN 978-1-5090-0626-7
Abstract
Recognition memory in a number of mammals is usually utilised to identify novel objects that violate model predictions. In humans in particular, the recognition of novel objects is foremost associated to their ability to group objects that are highly compatible/similar. Granular computing not only mimics the human cognition to draw objects together but also mimics the ability to capture associated properties by similarity, proximity or functionality. In this paper, an iterative information granulation approach is presented, for the problem of novelty detection in complex data. Two granular compatibility measures are used, based on principles of Granular Computing, namely the multidimensional distance between the granules, as well as the granular density and volume. A two-stage iterative information granulation is proposed in this work. In the first stage, a predefined number of granular detectors are constructed. The granular detectors capture the relationships (rules) between the input-output data and then use this information in a second granulation stage in order to discriminate new samples as novel. The proposed iterative information granulation approach for novelty detection is then applied to three different benchmark problems in pattern recognition demonstrating very good performance.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Detectors; Iterative methods; MIMICs; Indexes; Neural networks; Prototypes; Cognition |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 COMBILASER - 636902 INNOVATE UK (TSB) 101947 / 41205-293373 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 May 2017 13:56 |
Last Modified: | 21 Mar 2018 04:50 |
Published Version: | https://doi.org/10.1109/FUZZ-IEEE.2016.7737791 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/FUZZ-IEEE.2016.7737791 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:116415 |