A new multivariate decision tree construction algorithm based on variable precision rough set

Liang Zhang; Yun Ming Ye; Shui Yu; Fan Yuan Ma

doi:10.1007/978-3-540-45160-0_23

A new multivariate decision tree construction algorithm based on variable precision rough set

Liang Zhang^*
, Yun Ming Ye
, Shui Yu
, Fan Yuan Ma

^*Corresponding author for this work

Shanghai Jiao Tong University

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

2 Scopus citations

Abstract

In this paper we extend previous research and present a novel approach to construct multivariate decision tree, which has to some extent the ability of fault tolerance, by employing a development of RST, namely the variable precision rough sets (VPRS) model. Based on variable precision rough set theory, a new concept of generalization of one equivalence relation with respect to another one with precision β is introduced and used for construction of multivariate decision tree. The experimentation result shows its fitness to create multivariate decision tree retrieved from noisy data.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Guozhu Dong, Tang Changjie, Wei Wang
Publisher	Springer Verlag
Pages	238-246
Number of pages	9
ISBN (Electronic)	9783540407157
DOIs	https://doi.org/10.1007/978-3-540-45160-0_23
State	Published - 2003
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2762
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-45160-0_23

Cite this

Zhang, L., Ye, Y. M., Yu, S., & Ma, F. Y. (2003). A new multivariate decision tree construction algorithm based on variable precision rough set. In G. Dong, T. Changjie, & W. Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 238-246). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2762). Springer Verlag. https://doi.org/10.1007/978-3-540-45160-0_23

Zhang, Liang ; Ye, Yun Ming ; Yu, Shui et al. / A new multivariate decision tree construction algorithm based on variable precision rough set. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Guozhu Dong ; Tang Changjie ; Wei Wang. Springer Verlag, 2003. pp. 238-246 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A new multivariate decision tree construction algorithm based on variable precision rough set",

abstract = "In this paper we extend previous research and present a novel approach to construct multivariate decision tree, which has to some extent the ability of fault tolerance, by employing a development of RST, namely the variable precision rough sets (VPRS) model. Based on variable precision rough set theory, a new concept of generalization of one equivalence relation with respect to another one with precision β is introduced and used for construction of multivariate decision tree. The experimentation result shows its fitness to create multivariate decision tree retrieved from noisy data.",

author = "Liang Zhang and Ye, \{Yun Ming\} and Shui Yu and Ma, \{Fan Yuan\}",

year = "2003",

doi = "10.1007/978-3-540-45160-0\_23",

language = "英语",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "238--246",

editor = "Guozhu Dong and Tang Changjie and Wei Wang",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

address = "德国",

}

Zhang, L, Ye, YM, Yu, S & Ma, FY 2003, A new multivariate decision tree construction algorithm based on variable precision rough set. in G Dong, T Changjie & W Wang (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2762, Springer Verlag, pp. 238-246. https://doi.org/10.1007/978-3-540-45160-0_23

A new multivariate decision tree construction algorithm based on variable precision rough set. / Zhang, Liang; Ye, Yun Ming; Yu, Shui et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Guozhu Dong; Tang Changjie; Wei Wang. Springer Verlag, 2003. p. 238-246 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2762).

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

TY - CHAP

T1 - A new multivariate decision tree construction algorithm based on variable precision rough set

AU - Zhang, Liang

AU - Ye, Yun Ming

AU - Yu, Shui

AU - Ma, Fan Yuan

PY - 2003

Y1 - 2003

N2 - In this paper we extend previous research and present a novel approach to construct multivariate decision tree, which has to some extent the ability of fault tolerance, by employing a development of RST, namely the variable precision rough sets (VPRS) model. Based on variable precision rough set theory, a new concept of generalization of one equivalence relation with respect to another one with precision β is introduced and used for construction of multivariate decision tree. The experimentation result shows its fitness to create multivariate decision tree retrieved from noisy data.

AB - In this paper we extend previous research and present a novel approach to construct multivariate decision tree, which has to some extent the ability of fault tolerance, by employing a development of RST, namely the variable precision rough sets (VPRS) model. Based on variable precision rough set theory, a new concept of generalization of one equivalence relation with respect to another one with precision β is introduced and used for construction of multivariate decision tree. The experimentation result shows its fitness to create multivariate decision tree retrieved from noisy data.

UR - https://www.scopus.com/pages/publications/35048855993

U2 - 10.1007/978-3-540-45160-0_23

DO - 10.1007/978-3-540-45160-0_23

M3 - 章节

AN - SCOPUS:35048855993

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 238

EP - 246

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Dong, Guozhu

A2 - Changjie, Tang

A2 - Wang, Wei

PB - Springer Verlag

ER -

Zhang L, Ye YM, Yu S, Ma FY. A new multivariate decision tree construction algorithm based on variable precision rough set. In Dong G, Changjie T, Wang W, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2003. p. 238-246. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-45160-0_23

A new multivariate decision tree construction algorithm based on variable precision rough set

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