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Collaborative decision making by ensemble rule based classification systems

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Standard

Collaborative decision making by ensemble rule based classification systems. / Liu, Han; Gegov, Alexander Emilov.

Granular computing and decision-making: interactive and iterative approaches. ed. / Witold Pedrycz; Shyi-Ming Chen. Switzerland : Springer, 2015. p. 245-264 (Studies in Big Data; Vol. 10).

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Harvard

Liu, H & Gegov, AE 2015, Collaborative decision making by ensemble rule based classification systems. in W Pedrycz & S-M Chen (eds), Granular computing and decision-making: interactive and iterative approaches. Studies in Big Data, vol. 10, Springer, Switzerland, pp. 245-264 . https://doi.org/10.1007/978-3-319-16829-6_10

APA

Liu, H., & Gegov, A. E. (2015). Collaborative decision making by ensemble rule based classification systems. In W. Pedrycz, & S-M. Chen (Eds.), Granular computing and decision-making: interactive and iterative approaches (pp. 245-264 ). (Studies in Big Data; Vol. 10). Springer. https://doi.org/10.1007/978-3-319-16829-6_10

Vancouver

Liu H, Gegov AE. Collaborative decision making by ensemble rule based classification systems. In Pedrycz W, Chen S-M, editors, Granular computing and decision-making: interactive and iterative approaches. Switzerland: Springer. 2015. p. 245-264 . (Studies in Big Data). https://doi.org/10.1007/978-3-319-16829-6_10

Author

Liu, Han ; Gegov, Alexander Emilov. / Collaborative decision making by ensemble rule based classification systems. Granular computing and decision-making: interactive and iterative approaches. editor / Witold Pedrycz ; Shyi-Ming Chen. Switzerland : Springer, 2015. pp. 245-264 (Studies in Big Data).

Bibtex

@inbook{9e09b87476b7403d8d91ba3d1e6cada2,
title = "Collaborative decision making by ensemble rule based classification systems",
abstract = "Rule based classification is a popular approach for decision making. It is also achievable that multiple rule based classifiers work together for group deci-sion making by using ensemble learning approach. This kind of expert system is referred to as ensemble rule based classification system by means of a system of systems. In machine learning, an ensemble learning approach is usually adopted in order to improve overall predictive accuracy, which means to provide highly trusted decisions. This chapter introduces basic concepts of ensemble learning and reviews Random Prism to analyze its performance. This chapter also introduces an extended framework of ensemble learning, which is referred to as Collaborative and Compet-itive Random Decision Rules (CCRDR) and includes Information Entropy Based Rule Generation (IEBRG) and original Prism in addition to PrismTCS as base clas-sifiers. This is in order to overcome the identified limitations of Random Prism. Each of the base classifiers mentioned above is also introduced with respects to its essence and applications. An experimental study is undertaken towards comparative validation between the CCRDR and Random Prism. Contributions and Ongoing and future works are also highlighted.",
keywords = "Data Mining, Machine Learning , Rule Based Classification, Ensemble Learning, Collaborative Decision Making, Random Prism",
author = "Han Liu and Gegov, {Alexander Emilov}",
year = "2015",
doi = "10.1007/978-3-319-16829-6_10",
language = "English",
isbn = "9783319168289",
series = "Studies in Big Data",
publisher = "Springer",
pages = "245--264 ",
editor = "Pedrycz, {Witold } and Shyi-Ming Chen",
booktitle = "Granular computing and decision-making",

}

RIS

TY - CHAP

T1 - Collaborative decision making by ensemble rule based classification systems

AU - Liu, Han

AU - Gegov, Alexander Emilov

PY - 2015

Y1 - 2015

N2 - Rule based classification is a popular approach for decision making. It is also achievable that multiple rule based classifiers work together for group deci-sion making by using ensemble learning approach. This kind of expert system is referred to as ensemble rule based classification system by means of a system of systems. In machine learning, an ensemble learning approach is usually adopted in order to improve overall predictive accuracy, which means to provide highly trusted decisions. This chapter introduces basic concepts of ensemble learning and reviews Random Prism to analyze its performance. This chapter also introduces an extended framework of ensemble learning, which is referred to as Collaborative and Compet-itive Random Decision Rules (CCRDR) and includes Information Entropy Based Rule Generation (IEBRG) and original Prism in addition to PrismTCS as base clas-sifiers. This is in order to overcome the identified limitations of Random Prism. Each of the base classifiers mentioned above is also introduced with respects to its essence and applications. An experimental study is undertaken towards comparative validation between the CCRDR and Random Prism. Contributions and Ongoing and future works are also highlighted.

AB - Rule based classification is a popular approach for decision making. It is also achievable that multiple rule based classifiers work together for group deci-sion making by using ensemble learning approach. This kind of expert system is referred to as ensemble rule based classification system by means of a system of systems. In machine learning, an ensemble learning approach is usually adopted in order to improve overall predictive accuracy, which means to provide highly trusted decisions. This chapter introduces basic concepts of ensemble learning and reviews Random Prism to analyze its performance. This chapter also introduces an extended framework of ensemble learning, which is referred to as Collaborative and Compet-itive Random Decision Rules (CCRDR) and includes Information Entropy Based Rule Generation (IEBRG) and original Prism in addition to PrismTCS as base clas-sifiers. This is in order to overcome the identified limitations of Random Prism. Each of the base classifiers mentioned above is also introduced with respects to its essence and applications. An experimental study is undertaken towards comparative validation between the CCRDR and Random Prism. Contributions and Ongoing and future works are also highlighted.

KW - Data Mining

KW - Machine Learning

KW - Rule Based Classification

KW - Ensemble Learning

KW - Collaborative Decision Making

KW - Random Prism

U2 - 10.1007/978-3-319-16829-6_10

DO - 10.1007/978-3-319-16829-6_10

M3 - Chapter (peer-reviewed)

SN - 9783319168289

T3 - Studies in Big Data

SP - 245

EP - 264

BT - Granular computing and decision-making

A2 - Pedrycz, Witold

A2 - Chen, Shyi-Ming

PB - Springer

CY - Switzerland

ER -

ID: 2306220