Automatic rule set modeling and change detection of systems

Junichi Murata, Kiyosumi Ideta, Kotaro Hirasawa

研究成果: ジャーナルへの寄稿Conference article

抄録

An incremental inductive learning and change detection method is proposed which generates rule sets that contain general rules underlying the observed data and detects changes in them. Unlike most of other algorithms, the method is an incremental algorithm that generates the rule set in the course of data observation. The method is motivated by a demand for an automatic procedure for quick modeling of systems subject to change and detecting the changes. The method starts with a most general rule and performs specialization of the rules when a conflicting piece of data is observed. Also the unification of over-specialized rules is done to enhance the generality. The change detection is based on a statistical hypothesis test of a change in error rate of the rule set assuming randomness in the data presentation order. Simulation studies have been carried out on a toy problem, where a cat observes a mouse and builds a rule set describing the mouse's behavior while the mouse disappears and a new one comes in. The results show that the method can yield a general rule set and that it can successfully detect the change of the mice.

元の言語英語
ページ(範囲)3889-3894
ページ数6
ジャーナルProceedings of the IEEE International Conference on Systems, Man and Cybernetics
4
出版物ステータス出版済み - 12 1 1997
イベントProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - Orlando, FL, USA
継続期間: 10 12 199710 15 1997

Fingerprint

Statistical tests
modeling
detection method
learning
detection
method
simulation
demand
rate
test

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture

これを引用

Automatic rule set modeling and change detection of systems. / Murata, Junichi; Ideta, Kiyosumi; Hirasawa, Kotaro.

:: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 巻 4, 01.12.1997, p. 3889-3894.

研究成果: ジャーナルへの寄稿Conference article

@article{cc2b46f994f442948f482721cfe35357,
title = "Automatic rule set modeling and change detection of systems",
abstract = "An incremental inductive learning and change detection method is proposed which generates rule sets that contain general rules underlying the observed data and detects changes in them. Unlike most of other algorithms, the method is an incremental algorithm that generates the rule set in the course of data observation. The method is motivated by a demand for an automatic procedure for quick modeling of systems subject to change and detecting the changes. The method starts with a most general rule and performs specialization of the rules when a conflicting piece of data is observed. Also the unification of over-specialized rules is done to enhance the generality. The change detection is based on a statistical hypothesis test of a change in error rate of the rule set assuming randomness in the data presentation order. Simulation studies have been carried out on a toy problem, where a cat observes a mouse and builds a rule set describing the mouse's behavior while the mouse disappears and a new one comes in. The results show that the method can yield a general rule set and that it can successfully detect the change of the mice.",
author = "Junichi Murata and Kiyosumi Ideta and Kotaro Hirasawa",
year = "1997",
month = "12",
day = "1",
language = "English",
volume = "4",
pages = "3889--3894",
journal = "Proceedings of the IEEE International Conference on Systems, Man and Cybernetics",
issn = "0884-3627",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Automatic rule set modeling and change detection of systems

AU - Murata, Junichi

AU - Ideta, Kiyosumi

AU - Hirasawa, Kotaro

PY - 1997/12/1

Y1 - 1997/12/1

N2 - An incremental inductive learning and change detection method is proposed which generates rule sets that contain general rules underlying the observed data and detects changes in them. Unlike most of other algorithms, the method is an incremental algorithm that generates the rule set in the course of data observation. The method is motivated by a demand for an automatic procedure for quick modeling of systems subject to change and detecting the changes. The method starts with a most general rule and performs specialization of the rules when a conflicting piece of data is observed. Also the unification of over-specialized rules is done to enhance the generality. The change detection is based on a statistical hypothesis test of a change in error rate of the rule set assuming randomness in the data presentation order. Simulation studies have been carried out on a toy problem, where a cat observes a mouse and builds a rule set describing the mouse's behavior while the mouse disappears and a new one comes in. The results show that the method can yield a general rule set and that it can successfully detect the change of the mice.

AB - An incremental inductive learning and change detection method is proposed which generates rule sets that contain general rules underlying the observed data and detects changes in them. Unlike most of other algorithms, the method is an incremental algorithm that generates the rule set in the course of data observation. The method is motivated by a demand for an automatic procedure for quick modeling of systems subject to change and detecting the changes. The method starts with a most general rule and performs specialization of the rules when a conflicting piece of data is observed. Also the unification of over-specialized rules is done to enhance the generality. The change detection is based on a statistical hypothesis test of a change in error rate of the rule set assuming randomness in the data presentation order. Simulation studies have been carried out on a toy problem, where a cat observes a mouse and builds a rule set describing the mouse's behavior while the mouse disappears and a new one comes in. The results show that the method can yield a general rule set and that it can successfully detect the change of the mice.

UR - http://www.scopus.com/inward/record.url?scp=0031335296&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031335296&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0031335296

VL - 4

SP - 3889

EP - 3894

JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

SN - 0884-3627

ER -