Feature discriminability for pattern classification based on neural incremental attribute learning

Ting Wang; Sheng Uei Guan; Fei Liu

doi:10.1007/978-3-642-25664-6_32

Feature discriminability for pattern classification based on neural incremental attribute learning

Ting Wang^*
, Sheng Uei Guan
, Fei Liu

^*Corresponding author for this work

University of Liverpool
Xi'an Jiaotong-Liverpool University
La Trobe University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

17 Citations (Scopus)

Abstract

Feature ordering is important in Incremental Attribute Learning where features are gradually trained in one or more size. Apart from time-consuming contribution-based feature ordering methods, feature ordering also can be derived by filter criteria. In this paper, a novel criterion based on a new metric called Discriminability is presented to give ranks for feature ordering. Final results show that the new metric not only is applicable for IAL, but also exhibits better performance in lower error rates.

Original language	English
Title of host publication	Foundations of Intelligent Systems
Subtitle of host publication	Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011)
Editors	Yinglin Wang, Tianrui Li
Pages	275-280
Number of pages	6
DOIs	https://doi.org/10.1007/978-3-642-25664-6_32
Publication status	Published - 2011

Publication series

Name	Advances in Intelligent and Soft Computing
Volume	122
ISSN (Print)	1867-5662

Keywords

feature ordering
incremental attribute learning
neural networks

Access to Document

10.1007/978-3-642-25664-6_32

Cite this

Wang, T., Guan, S. U., & Liu, F. (2011). Feature discriminability for pattern classification based on neural incremental attribute learning. In Y. Wang, & T. Li (Eds.), Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011) (pp. 275-280). (Advances in Intelligent and Soft Computing; Vol. 122). https://doi.org/10.1007/978-3-642-25664-6_32

Wang, Ting ; Guan, Sheng Uei ; Liu, Fei. / Feature discriminability for pattern classification based on neural incremental attribute learning. Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011). editor / Yinglin Wang ; Tianrui Li. 2011. pp. 275-280 (Advances in Intelligent and Soft Computing).

@inproceedings{77475de8305545cb962c363528dff7ea,

title = "Feature discriminability for pattern classification based on neural incremental attribute learning",

abstract = "Feature ordering is important in Incremental Attribute Learning where features are gradually trained in one or more size. Apart from time-consuming contribution-based feature ordering methods, feature ordering also can be derived by filter criteria. In this paper, a novel criterion based on a new metric called Discriminability is presented to give ranks for feature ordering. Final results show that the new metric not only is applicable for IAL, but also exhibits better performance in lower error rates.",

keywords = "feature ordering, incremental attribute learning, neural networks",

author = "Ting Wang and Guan, \{Sheng Uei\} and Fei Liu",

year = "2011",

doi = "10.1007/978-3-642-25664-6\_32",

language = "English",

isbn = "9783642256639",

series = "Advances in Intelligent and Soft Computing",

pages = "275--280",

editor = "Yinglin Wang and Tianrui Li",

booktitle = "Foundations of Intelligent Systems",

}

Wang, T, Guan, SU & Liu, F 2011, Feature discriminability for pattern classification based on neural incremental attribute learning. in Y Wang & T Li (eds), Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011). Advances in Intelligent and Soft Computing, vol. 122, pp. 275-280. https://doi.org/10.1007/978-3-642-25664-6_32

Feature discriminability for pattern classification based on neural incremental attribute learning. / Wang, Ting; Guan, Sheng Uei; Liu, Fei.
Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011). ed. / Yinglin Wang; Tianrui Li. 2011. p. 275-280 (Advances in Intelligent and Soft Computing; Vol. 122).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Liu, Fei

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AB - Feature ordering is important in Incremental Attribute Learning where features are gradually trained in one or more size. Apart from time-consuming contribution-based feature ordering methods, feature ordering also can be derived by filter criteria. In this paper, a novel criterion based on a new metric called Discriminability is presented to give ranks for feature ordering. Final results show that the new metric not only is applicable for IAL, but also exhibits better performance in lower error rates.

KW - feature ordering

KW - incremental attribute learning

KW - neural networks

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A2 - Wang, Yinglin

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Wang T, Guan SU, Liu F. Feature discriminability for pattern classification based on neural incremental attribute learning. In Wang Y, Li T, editors, Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011). 2011. p. 275-280. (Advances in Intelligent and Soft Computing). doi: 10.1007/978-3-642-25664-6_32

Feature discriminability for pattern classification based on neural incremental attribute learning

Abstract

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Keywords

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