Rapid anomaly detection using integrated prudence analysis (IPA)

Omaru Maruatona; Peter Vamplew; Richard Dazeley; Paul A. Watters

doi:10.1007/978-3-030-04503-6_12

Rapid anomaly detection using integrated prudence analysis (IPA)

Omaru Maruatona^*, Peter Vamplew, Richard Dazeley, Paul A. Watters

^*Corresponding author for this work

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

Abstract

Integrated Prudence Analysis has been proposed as a method to maximize the accuracy of rule based systems. The paper presents evaluation results of the three Prudence methods on public datasets which demonstrate that combining attribute-based and structural Prudence produces a net improvement in Prudence Accuracy.

Original language	English
Title of host publication	Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers
Editors	Mohadeseh Ganji, Lida Rashidi, Benjamin C. M. Fung, Can Wang
Place of Publication	Cham, Switzerland
Publisher	Springer, Springer Nature
Pages	137-141
Number of pages	5
ISBN (Electronic)	9783030045036
ISBN (Print)	9783030045029
DOIs	https://doi.org/10.1007/978-3-030-04503-6_12
Publication status	Published - 2018
Externally published	Yes
Event	22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia Duration: 3 Jun 2018 → 3 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11154
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018
Country/Territory	Australia
City	Melbourne
Period	3/06/18 → 3/06/18

Keywords

Expert systems
Integrated prudence
Prudence analysis

Access to Document

10.1007/978-3-030-04503-6_12

Cite this

Maruatona, O., Vamplew, P., Dazeley, R., & Watters, P. A. (2018). Rapid anomaly detection using integrated prudence analysis (IPA). In M. Ganji, L. Rashidi, B. C. M. Fung, & C. Wang (Eds.), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers (pp. 137-141). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11154). Springer, Springer Nature. https://doi.org/10.1007/978-3-030-04503-6_12

Maruatona, Omaru ; Vamplew, Peter ; Dazeley, Richard et al. / Rapid anomaly detection using integrated prudence analysis (IPA). Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers. editor / Mohadeseh Ganji ; Lida Rashidi ; Benjamin C. M. Fung ; Can Wang. Cham, Switzerland : Springer, Springer Nature, 2018. pp. 137-141 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Rapid anomaly detection using integrated prudence analysis (IPA)",

abstract = "Integrated Prudence Analysis has been proposed as a method to maximize the accuracy of rule based systems. The paper presents evaluation results of the three Prudence methods on public datasets which demonstrate that combining attribute-based and structural Prudence produces a net improvement in Prudence Accuracy.",

keywords = "Expert systems, Integrated prudence, Prudence analysis",

author = "Omaru Maruatona and Peter Vamplew and Richard Dazeley and Watters, {Paul A.}",

year = "2018",

doi = "10.1007/978-3-030-04503-6_12",

language = "English",

isbn = "9783030045029",

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

publisher = "Springer, Springer Nature",

pages = "137--141",

editor = "Mohadeseh Ganji and Lida Rashidi and Fung, {Benjamin C. M.} and Can Wang",

booktitle = "Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers",

address = "United States",

note = "22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018 ; Conference date: 03-06-2018 Through 03-06-2018",

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Maruatona, O, Vamplew, P, Dazeley, R & Watters, PA 2018, Rapid anomaly detection using integrated prudence analysis (IPA). in M Ganji, L Rashidi, BCM Fung & C Wang (eds), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11154, Springer, Springer Nature, Cham, Switzerland, pp. 137-141, 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, Australia, 3/06/18. https://doi.org/10.1007/978-3-030-04503-6_12

Rapid anomaly detection using integrated prudence analysis (IPA). / Maruatona, Omaru; Vamplew, Peter; Dazeley, Richard et al.
Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers. ed. / Mohadeseh Ganji; Lida Rashidi; Benjamin C. M. Fung; Can Wang. Cham, Switzerland: Springer, Springer Nature, 2018. p. 137-141 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11154).

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

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T1 - Rapid anomaly detection using integrated prudence analysis (IPA)

AU - Maruatona, Omaru

AU - Vamplew, Peter

AU - Dazeley, Richard

AU - Watters, Paul A.

PY - 2018

Y1 - 2018

N2 - Integrated Prudence Analysis has been proposed as a method to maximize the accuracy of rule based systems. The paper presents evaluation results of the three Prudence methods on public datasets which demonstrate that combining attribute-based and structural Prudence produces a net improvement in Prudence Accuracy.

AB - Integrated Prudence Analysis has been proposed as a method to maximize the accuracy of rule based systems. The paper presents evaluation results of the three Prudence methods on public datasets which demonstrate that combining attribute-based and structural Prudence produces a net improvement in Prudence Accuracy.

KW - Expert systems

KW - Integrated prudence

KW - Prudence analysis

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DO - 10.1007/978-3-030-04503-6_12

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AN - SCOPUS:85059070091

SN - 9783030045029

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

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EP - 141

BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers

A2 - Ganji, Mohadeseh

A2 - Rashidi, Lida

A2 - Fung, Benjamin C. M.

A2 - Wang, Can

PB - Springer, Springer Nature

CY - Cham, Switzerland

T2 - 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018

Y2 - 3 June 2018 through 3 June 2018

ER -

Maruatona O, Vamplew P, Dazeley R, Watters PA. Rapid anomaly detection using integrated prudence analysis (IPA). In Ganji M, Rashidi L, Fung BCM, Wang C, editors, Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers. Cham, Switzerland: Springer, Springer Nature. 2018. p. 137-141. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-04503-6_12

Rapid anomaly detection using integrated prudence analysis (IPA)

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