Detection of CAN by ensemble classifiers based on ripple down rules

Andrei Kelarev; Richard Dazeley; Andrew Stranieri; John Yearwood; Herbert Jelinek

doi:10.1007/978-3-642-32541-0_13

Detection of CAN by ensemble classifiers based on ripple down rules

Andrei Kelarev
, Richard Dazeley
, Andrew Stranieri
, John Yearwood
, Herbert Jelinek

Biomedical Engineering & Biotechnology

University of Ballarat, The Centre for Informatics and Applied Optimization

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

11 Scopus citations

Abstract

It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.

Original language	British English
Title of host publication	Knowledge Management and Acquisition for Intelligent Systems - 12th Pacific Rim Knowledge Acquisition Workshop, PKAW 2012, Proceedings
Editors	Deborah Richards, Byeong Ho Kang
Publisher	Springer Verlag
Pages	147-159
Number of pages	13
ISBN (Print)	9783642325403
DOIs	https://doi.org/10.1007/978-3-642-32541-0_13
State	Published - 2012
Event	12th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2012, held in conjunction with 12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012 - Kuching, Malaysia Duration: 5 Sep 2012 → 6 Sep 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7457 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2012, held in conjunction with 12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012
Country/Territory	Malaysia
City	Kuching
Period	5/09/12 → 6/09/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1007/978-3-642-32541-0_13

OpenUrl availability

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Kelarev, A., Dazeley, R., Stranieri, A., Yearwood, J., & Jelinek, H. (2012). Detection of CAN by ensemble classifiers based on ripple down rules. In D. Richards, & B. H. Kang (Eds.), Knowledge Management and Acquisition for Intelligent Systems - 12th Pacific Rim Knowledge Acquisition Workshop, PKAW 2012, Proceedings (pp. 147-159). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7457 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-642-32541-0_13

Kelarev, Andrei ; Dazeley, Richard ; Stranieri, Andrew et al. / Detection of CAN by ensemble classifiers based on ripple down rules. Knowledge Management and Acquisition for Intelligent Systems - 12th Pacific Rim Knowledge Acquisition Workshop, PKAW 2012, Proceedings. editor / Deborah Richards ; Byeong Ho Kang. Springer Verlag, 2012. pp. 147-159 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Detection of CAN by ensemble classifiers based on ripple down rules",

abstract = "It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.",

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Kelarev, A, Dazeley, R, Stranieri, A, Yearwood, J & Jelinek, H 2012, Detection of CAN by ensemble classifiers based on ripple down rules. in D Richards & BH Kang (eds), Knowledge Management and Acquisition for Intelligent Systems - 12th Pacific Rim Knowledge Acquisition Workshop, PKAW 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7457 LNAI, Springer Verlag, pp. 147-159, 12th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2012, held in conjunction with 12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012, Kuching, Malaysia, 5/09/12. https://doi.org/10.1007/978-3-642-32541-0_13

Detection of CAN by ensemble classifiers based on ripple down rules. / Kelarev, Andrei; Dazeley, Richard; Stranieri, Andrew et al.
Knowledge Management and Acquisition for Intelligent Systems - 12th Pacific Rim Knowledge Acquisition Workshop, PKAW 2012, Proceedings. ed. / Deborah Richards; Byeong Ho Kang. Springer Verlag, 2012. p. 147-159 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7457 LNAI).

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

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AB - It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.

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Kelarev A, Dazeley R, Stranieri A, Yearwood J, Jelinek H. Detection of CAN by ensemble classifiers based on ripple down rules. In Richards D, Kang BH, editors, Knowledge Management and Acquisition for Intelligent Systems - 12th Pacific Rim Knowledge Acquisition Workshop, PKAW 2012, Proceedings. Springer Verlag. 2012. p. 147-159. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-32541-0_13

Detection of CAN by ensemble classifiers based on ripple down rules

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UN SDGs

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