The ballarat incremental knowledge engine
- Dazeley, Richard, Warner, Philip, Johnson, Scott, Vamplew, Peter
- Authors: Dazeley, Richard , Warner, Philip , Johnson, Scott , Vamplew, Peter
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper pressented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 195-207
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- Description: Ripple Down Rules (RDR) is a maturing collection of methodologies for the incremental development and maintenance of medium to large rule-based knowledge systems. While earlier knowledge based systems relied on extensive modeling and knowledge engineering, RDR instead takes a simple no-model approach that merges the development and maintenance stages. Over the last twenty years RDR has been significantly expanded and applied in numerous domains. Until now researchers have generally implemented their own version of the methodologies, while commercial implementations are not made available. This has resulted in much duplicated code and the advantages of RDR not being available to a wider audience. The aim of this project is to develop a comprehensive and extensible platform that supports current and future RDR technologies, thereby allowing researchers and developers access to the power and versatility of RDR. This paper is a report on the current status of the project and marks the first release of the software. © 2010 Springer-Verlag Berlin Heidelberg.
- Authors: Dazeley, Richard , Warner, Philip , Johnson, Scott , Vamplew, Peter
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper pressented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 195-207
- Full Text:
- Reviewed:
- Description: Ripple Down Rules (RDR) is a maturing collection of methodologies for the incremental development and maintenance of medium to large rule-based knowledge systems. While earlier knowledge based systems relied on extensive modeling and knowledge engineering, RDR instead takes a simple no-model approach that merges the development and maintenance stages. Over the last twenty years RDR has been significantly expanded and applied in numerous domains. Until now researchers have generally implemented their own version of the methodologies, while commercial implementations are not made available. This has resulted in much duplicated code and the advantages of RDR not being available to a wider audience. The aim of this project is to develop a comprehensive and extensible platform that supports current and future RDR technologies, thereby allowing researchers and developers access to the power and versatility of RDR. This paper is a report on the current status of the project and marks the first release of the software. © 2010 Springer-Verlag Berlin Heidelberg.
Consensus clustering and supervised classification for profiling phishing emails in internet commerce security
- Dazeley, Richard, Yearwood, John, Kang, Byeongho, Kelarev, Andrei
- Authors: Dazeley, Richard , Yearwood, John , Kang, Byeongho , Kelarev, Andrei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 235-246
- Full Text:
- Reviewed:
- Description: This article investigates internet commerce security applications of a novel combined method, which uses unsupervised consensus clustering algorithms in combination with supervised classification methods. First, a variety of independent clustering algorithms are applied to a randomized sample of data. Second, several consensus functions and sophisticated algorithms are used to combine these independent clusterings into one final consensus clustering. Third, the consensus clustering of the randomized sample is used as a training set to train several fast supervised classification algorithms. Finally, these fast classification algorithms are used to classify the whole large data set. One of the advantages of this approach is in its ability to facilitate the inclusion of contributions from domain experts in order to adjust the training set created by consensus clustering. We apply this approach to profiling phishing emails selected from a very large data set supplied by the industry partners of the Centre for Informatics and Applied Optimization. Our experiments compare the performance of several classification algorithms incorporated in this scheme. © 2010 Springer-Verlag Berlin Heidelberg.
- Authors: Dazeley, Richard , Yearwood, John , Kang, Byeongho , Kelarev, Andrei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 235-246
- Full Text:
- Reviewed:
- Description: This article investigates internet commerce security applications of a novel combined method, which uses unsupervised consensus clustering algorithms in combination with supervised classification methods. First, a variety of independent clustering algorithms are applied to a randomized sample of data. Second, several consensus functions and sophisticated algorithms are used to combine these independent clusterings into one final consensus clustering. Third, the consensus clustering of the randomized sample is used as a training set to train several fast supervised classification algorithms. Finally, these fast classification algorithms are used to classify the whole large data set. One of the advantages of this approach is in its ability to facilitate the inclusion of contributions from domain experts in order to adjust the training set created by consensus clustering. We apply this approach to profiling phishing emails selected from a very large data set supplied by the industry partners of the Centre for Informatics and Applied Optimization. Our experiments compare the performance of several classification algorithms incorporated in this scheme. © 2010 Springer-Verlag Berlin Heidelberg.
Business advisor knowledge and knowledge transference : A conceptual framework
- Labas, Alan, Courvisanos, Jerry, Henson, Sam
- Authors: Labas, Alan , Courvisanos, Jerry , Henson, Sam
- Date: 2015
- Type: Text , Conference paper
- Relation: 28th Annual SEAANZ Conference Proceedings; Melbourne, Australia; 1st-3rd July 2015. p. 1-17
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- Description: Prior studies raise the question of how business advisors’ knowledge affects the provision of advice to small business. This paper recognises there is limited understanding of ‘how knowledge is connected to action’ and asks the question of how to research such an issue. A conceptual framework is derived from the literature to guide future empirical analysis exploring small business advisor knowledge and its transference. Two theories underpin this framework and illustrate the important role external advisors play in small business knowledge development - the theory of outside assistance as a knowledge resource, and theory of guided preparation as a guide to action based on advisor knowledge. The framework is underpinned by a critical realist methodology that allows actors (i.e. small business advisors) to operate in a changing environment. This critical realist philosophical lens enables the framework to uncover causal relationship between professional small business advisor knowledge foundations and knowledge transference.
- Authors: Labas, Alan , Courvisanos, Jerry , Henson, Sam
- Date: 2015
- Type: Text , Conference paper
- Relation: 28th Annual SEAANZ Conference Proceedings; Melbourne, Australia; 1st-3rd July 2015. p. 1-17
- Full Text:
- Reviewed:
- Description: Prior studies raise the question of how business advisors’ knowledge affects the provision of advice to small business. This paper recognises there is limited understanding of ‘how knowledge is connected to action’ and asks the question of how to research such an issue. A conceptual framework is derived from the literature to guide future empirical analysis exploring small business advisor knowledge and its transference. Two theories underpin this framework and illustrate the important role external advisors play in small business knowledge development - the theory of outside assistance as a knowledge resource, and theory of guided preparation as a guide to action based on advisor knowledge. The framework is underpinned by a critical realist methodology that allows actors (i.e. small business advisors) to operate in a changing environment. This critical realist philosophical lens enables the framework to uncover causal relationship between professional small business advisor knowledge foundations and knowledge transference.
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