Coupled fuzzy k-nearest neighbors classification of imbalanced non-IID categorical data

Liu, C; Cao, L; Yu, PS

Coupled fuzzy k-nearest neighbors classification of imbalanced non-IID categorical data

Liu, C Cao, L

Yu, PS

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2014, pp. 1122 - 1129
Issue Date:: 2014-09-03

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Field	Value	Language
dc.contributor.author	Liu, C	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Yu, PS	en_US
dc.date.issued	2014-09-03	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2014, pp. 1122 - 1129	en_US
dc.identifier.isbn	9781479914845	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119829
dc.description.abstract	© 2014 IEEE. Mining imbalanced data has recently received increasing attention due to its challenge and wide applications in the real world. Most of the existing work focuses on numerical data by manipulating the data structure which essentially changes the data characteristics or developing new distance or similarity measures which are designed for data with the so-called IID assumption, namely data is independent and identically distributed. This is not consistent with the real-life data and business needs, which request to fully respect the data structure and coupling relationships embedded in data objects, features and feature values. In this paper, we propose a novel coupled fuzzy similarity-based classification approach to cater for the difference between classes by a fuzzy membership and the couplings by coupled object similarity, and incorporate them into the most popular classifier: kNN to form a coupled fuzzy kNN (ie. CF-kNN). We test the approach on 14 categorical data sets compared to several kNN variants and classic classifiers including C4.5 and NaiveBayes. The experimental results show that CF-kNN outperforms the baselines, and those classifiers incorporated with the proposed coupled fuzzy similarity perform better than their original editions.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2014.6889773	en_US
dc.title	Coupled fuzzy k-nearest neighbors classification of imbalanced non-IID categorical data	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. Mining imbalanced data has recently received increasing attention due to its challenge and wide applications in the real world. Most of the existing work focuses on numerical data by manipulating the data structure which essentially changes the data characteristics or developing new distance or similarity measures which are designed for data with the so-called IID assumption, namely data is independent and identically distributed. This is not consistent with the real-life data and business needs, which request to fully respect the data structure and coupling relationships embedded in data objects, features and feature values. In this paper, we propose a novel coupled fuzzy similarity-based classification approach to cater for the difference between classes by a fuzzy membership and the couplings by coupled object similarity, and incorporate them into the most popular classifier: kNN to form a coupled fuzzy kNN (ie. CF-kNN). We test the approach on 14 categorical data sets compared to several kNN variants and classic classifiers including C4.5 and NaiveBayes. The experimental results show that CF-kNN outperforms the baselines, and those classifiers incorporated with the proposed coupled fuzzy similarity perform better than their original editions.

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http://hdl.handle.net/10453/119829