Learning similarity measure of nominal features in CBR classifiers

Yan Li; Chi Keung Simon Shiu; Sankar Kumar Pal; James Nga Kwok Liu

doi:10.1007/11590316_126

Learning similarity measure of nominal features in CBR classifiers

Yan Li, Chi Keung Simon Shiu, Sankar Kumar Pal, James Nga Kwok Liu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

3 Citations (Scopus)

Abstract

Nominal feature is one type of symbolic features, whose feature values are completely unordered. The most often used existing similarity metrics for symbolic features is the Hamming metric, where similarity computation is coarse-grained and may affect the performance of case retrieval and then the classification accuracy. This paper presents a GA-based approach for learning similarity measure of nominal features for CBR classifiers. Based on the learned similarities, the classification accuracy can be improved, and the importance of each nominal feature can be analyzed to enhance the understanding of the used data sets.

Original language	English
Title of host publication	Pattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings
Pages	780-785
Number of pages	6
DOIs	https://doi.org/10.1007/11590316_126
Publication status	Published - 1 Dec 2005
Event	1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005 - Kolkata, India Duration: 20 Dec 2005 → 22 Dec 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3776 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005
Country/Territory	India
City	Kolkata
Period	20/12/05 → 22/12/05

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

More information

10.1007/11590316_126

Cite this

Li, Y., Shiu, C. K. S., Pal, S. K., & Liu, J. N. K. (2005). Learning similarity measure of nominal features in CBR classifiers. In Pattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings (pp. 780-785). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3776 LNCS). https://doi.org/10.1007/11590316_126

Li, Yan ; Shiu, Chi Keung Simon ; Pal, Sankar Kumar et al. / Learning similarity measure of nominal features in CBR classifiers. Pattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings. 2005. pp. 780-785 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4e8f13d1468f4ebaaaa00541702eafaf,

title = "Learning similarity measure of nominal features in CBR classifiers",

abstract = "Nominal feature is one type of symbolic features, whose feature values are completely unordered. The most often used existing similarity metrics for symbolic features is the Hamming metric, where similarity computation is coarse-grained and may affect the performance of case retrieval and then the classification accuracy. This paper presents a GA-based approach for learning similarity measure of nominal features for CBR classifiers. Based on the learned similarities, the classification accuracy can be improved, and the importance of each nominal feature can be analyzed to enhance the understanding of the used data sets.",

author = "Yan Li and Shiu, {Chi Keung Simon} and Pal, {Sankar Kumar} and Liu, {James Nga Kwok}",

year = "2005",

month = dec,

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doi = "10.1007/11590316_126",

language = "English",

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pages = "780--785",

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note = "1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005 ; Conference date: 20-12-2005 Through 22-12-2005",

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Li, Y, Shiu, CKS, Pal, SK & Liu, JNK 2005, Learning similarity measure of nominal features in CBR classifiers. in Pattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3776 LNCS, pp. 780-785, 1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005, Kolkata, India, 20/12/05. https://doi.org/10.1007/11590316_126

Learning similarity measure of nominal features in CBR classifiers. / Li, Yan; Shiu, Chi Keung Simon; Pal, Sankar Kumar et al.
Pattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings. 2005. p. 780-785 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3776 LNCS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - Nominal feature is one type of symbolic features, whose feature values are completely unordered. The most often used existing similarity metrics for symbolic features is the Hamming metric, where similarity computation is coarse-grained and may affect the performance of case retrieval and then the classification accuracy. This paper presents a GA-based approach for learning similarity measure of nominal features for CBR classifiers. Based on the learned similarities, the classification accuracy can be improved, and the importance of each nominal feature can be analyzed to enhance the understanding of the used data sets.

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Li Y, Shiu CKS, Pal SK, Liu JNK. Learning similarity measure of nominal features in CBR classifiers. In Pattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings. 2005. p. 780-785. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11590316_126

Learning similarity measure of nominal features in CBR classifiers

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