Orientation distance-based discriminative feature extraction for multi-class classification

Liu, B; Xiao, Y; Cao, L; Yu, PS

Orientation distance-based discriminative feature extraction for multi-class classification

Liu, B Xiao, Y Cao, L

Yu, PS

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Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2010, pp. 909 - 918
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Liu, B	en_US
dc.contributor.author	Xiao, Y	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	2010-12-01	en_US
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2010, pp. 909 - 918	en_US
dc.identifier.isbn	9781450300995	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16225
dc.description.abstract	Feature extraction is an effective step in data mining and machine learning. While many feature extraction methods have been proposed for clustering, classification and regression, very limited work has been done on multi-class classification problems. In fact, the accuracy of multi-class classification problems relies on well-extracted features, the modeling part aside. This paper proposes a new feature extraction method, namely extracting orientation distance-based discriminative (ODD) features, which is particularly designed for multi-class classification problems. The proposed method works in two steps. In the first step, we extend the Fisher Discriminant idea to determine more appropriate kernel function and map the input data with all classes into a feature space. In the second step, the ODD features are extracted based on the one-vs-all scheme to generate discriminative features between a pattern and each hyper-plane. These newly extracted features are treated as the representative features and are further used in the subsequent classification procedure. Substantial experiments on both UCI and real-world datasets have been conducted to investigate the performance of ODD features based multi-class classification. The statistical results show that the classification accuracy based on ODD features outperforms that of the state-of-the-art feature extraction methods. © 2010 ACM.	en_US
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings	en_US
dc.relation.isbasedon	10.1145/1871437.1871553	en_US
dc.title	Orientation distance-based discriminative feature extraction for multi-class classification	en_US
dc.type	Conference Proceeding
utslib.for	080201 Analysis of Algorithms and Complexity	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Toronto, Ontario, Canada	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:

Feature extraction is an effective step in data mining and machine learning. While many feature extraction methods have been proposed for clustering, classification and regression, very limited work has been done on multi-class classification problems. In fact, the accuracy of multi-class classification problems relies on well-extracted features, the modeling part aside. This paper proposes a new feature extraction method, namely extracting orientation distance-based discriminative (ODD) features, which is particularly designed for multi-class classification problems. The proposed method works in two steps. In the first step, we extend the Fisher Discriminant idea to determine more appropriate kernel function and map the input data with all classes into a feature space. In the second step, the ODD features are extracted based on the one-vs-all scheme to generate discriminative features between a pattern and each hyper-plane. These newly extracted features are treated as the representative features and are further used in the subsequent classification procedure. Substantial experiments on both UCI and real-world datasets have been conducted to investigate the performance of ODD features based multi-class classification. The statistical results show that the classification accuracy based on ODD features outperforms that of the state-of-the-art feature extraction methods. © 2010 ACM.

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http://hdl.handle.net/10453/16225