Rule reduction over numerical attributes in decision trees using multilayer perceptron

Dae Eun Kim; Jaeho Lee

doi:10.1007/3-540-45357-1_57

Rule reduction over numerical attributes in decision trees using multilayer perceptron

Dae Eun Kim, Jaeho Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Many data sets show significant correlations between input variables, and much useful information is hidden in the data in a non- linear format. It has been shown that a neural network is better than a direct application of induction trees in modeling nonlinear characteristics of sample data. We have extracted a compact set of rules to support data with input variable relations over continuous-valued attributes. Those re- lations as a set of linear classifiers can be obtained from neural network modeling based on back-propagation. It is shown in this paper that vari- able thresholds play an important role in constructing linear classifier rules when we use a decision tree over linear classifiers extracted from a multilayer perceptron. We have tested this scheme over several data sets to compare it with the decision tree results.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings
Editors	David Cheung, Graham J. Williams, Qing Li
Publisher	Springer Verlag
Pages	538-549
Number of pages	12
ISBN (Print)	3540419101, 9783540419105
DOIs	https://doi.org/10.1007/3-540-45357-1_57
Publication status	Published - 2001
Event	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 - Kowloon, Hong Kong Duration: 2001 Apr 16 → 2001 Apr 18

Publication series

Name	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	2035
ISSN (Print)	0302-9743

Other

Other	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001
Country/Territory	Hong Kong
City	Kowloon
Period	01/4/16 → 01/4/18

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/3-540-45357-1_57

Cite this

Kim, D. E., & Lee, J. (2001). Rule reduction over numerical attributes in decision trees using multilayer perceptron. In D. Cheung, G. J. Williams, & Q. Li (Eds.), Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings (pp. 538-549). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2035). Springer Verlag. https://doi.org/10.1007/3-540-45357-1_57

Kim, Dae Eun ; Lee, Jaeho. / Rule reduction over numerical attributes in decision trees using multilayer perceptron. Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. editor / David Cheung ; Graham J. Williams ; Qing Li. Springer Verlag, 2001. pp. 538-549 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)).

@inproceedings{047ec8b8b9df4e5db473c8672965dc46,

title = "Rule reduction over numerical attributes in decision trees using multilayer perceptron",

abstract = "Many data sets show significant correlations between input variables, and much useful information is hidden in the data in a non- linear format. It has been shown that a neural network is better than a direct application of induction trees in modeling nonlinear characteristics of sample data. We have extracted a compact set of rules to support data with input variable relations over continuous-valued attributes. Those re- lations as a set of linear classifiers can be obtained from neural network modeling based on back-propagation. It is shown in this paper that vari- able thresholds play an important role in constructing linear classifier rules when we use a decision tree over linear classifiers extracted from a multilayer perceptron. We have tested this scheme over several data sets to compare it with the decision tree results.",

author = "Kim, {Dae Eun} and Jaeho Lee",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 ; Conference date: 16-04-2001 Through 18-04-2001",

year = "2001",

doi = "10.1007/3-540-45357-1_57",

language = "English",

isbn = "3540419101",

series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

publisher = "Springer Verlag",

pages = "538--549",

editor = "David Cheung and Williams, {Graham J.} and Qing Li",

booktitle = "Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings",

address = "Germany",

}

Kim, DE & Lee, J 2001, Rule reduction over numerical attributes in decision trees using multilayer perceptron. in D Cheung, GJ Williams & Q Li (eds), Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 2035, Springer Verlag, pp. 538-549, 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001, Kowloon, Hong Kong, 01/4/16. https://doi.org/10.1007/3-540-45357-1_57

Rule reduction over numerical attributes in decision trees using multilayer perceptron. / Kim, Dae Eun; Lee, Jaeho.

Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. ed. / David Cheung; Graham J. Williams; Qing Li. Springer Verlag, 2001. p. 538-549 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2035).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Rule reduction over numerical attributes in decision trees using multilayer perceptron

AU - Kim, Dae Eun

AU - Lee, Jaeho

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2001.

PY - 2001

Y1 - 2001

N2 - Many data sets show significant correlations between input variables, and much useful information is hidden in the data in a non- linear format. It has been shown that a neural network is better than a direct application of induction trees in modeling nonlinear characteristics of sample data. We have extracted a compact set of rules to support data with input variable relations over continuous-valued attributes. Those re- lations as a set of linear classifiers can be obtained from neural network modeling based on back-propagation. It is shown in this paper that vari- able thresholds play an important role in constructing linear classifier rules when we use a decision tree over linear classifiers extracted from a multilayer perceptron. We have tested this scheme over several data sets to compare it with the decision tree results.

AB - Many data sets show significant correlations between input variables, and much useful information is hidden in the data in a non- linear format. It has been shown that a neural network is better than a direct application of induction trees in modeling nonlinear characteristics of sample data. We have extracted a compact set of rules to support data with input variable relations over continuous-valued attributes. Those re- lations as a set of linear classifiers can be obtained from neural network modeling based on back-propagation. It is shown in this paper that vari- able thresholds play an important role in constructing linear classifier rules when we use a decision tree over linear classifiers extracted from a multilayer perceptron. We have tested this scheme over several data sets to compare it with the decision tree results.

UR - http://www.scopus.com/inward/record.url?scp=84942926848&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84942926848&partnerID=8YFLogxK

U2 - 10.1007/3-540-45357-1_57

DO - 10.1007/3-540-45357-1_57

M3 - Conference contribution

AN - SCOPUS:84942926848

SN - 3540419101

SN - 9783540419105

T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

SP - 538

EP - 549

BT - Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings

A2 - Cheung, David

A2 - Williams, Graham J.

A2 - Li, Qing

PB - Springer Verlag

T2 - 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001

Y2 - 16 April 2001 through 18 April 2001

ER -

Kim DE, Lee J. Rule reduction over numerical attributes in decision trees using multilayer perceptron. In Cheung D, Williams GJ, Li Q, editors, Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. Springer Verlag. 2001. p. 538-549. (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)). doi: 10.1007/3-540-45357-1_57

Rule reduction over numerical attributes in decision trees using multilayer perceptron

Abstract

Publication series

Other

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this